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class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Ayse Bener</h3></div><div class="js-work-strip profile--work_container" data-work-id="125713181"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125713181/Exploiting_the_Essential_Assumptions_of_Analogy_Based_Effort_Estimation"><img alt="Research paper thumbnail of Exploiting the Essential Assumptions of Analogy-Based Effort Estimation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125713181/Exploiting_the_Essential_Assumptions_of_Analogy_Based_Effort_Estimation">Exploiting the Essential Assumptions of Analogy-Based Effort Estimation</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Software Engineering</span><span>, 2012</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d58475e11e17ac5c3750cfb00252fbe6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119706466,"asset_id":125713181,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119706466/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper 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class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104412772/A_systematic_literature_review_on_the_applications_of_Bayesian_networks_to_predict_software_quality"><img alt="Research paper thumbnail of A systematic literature review on the applications of Bayesian networks to predict software quality" class="work-thumbnail" src="https://attachments.academia-assets.com/104150066/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104412772/A_systematic_literature_review_on_the_applications_of_Bayesian_networks_to_predict_software_quality">A systematic literature review on the applications of Bayesian networks to predict software quality</a></div><div class="wp-workCard_item"><span>Software Quality Journal</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5fdc370d03713bece2b09734f5b40258" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104150066,"asset_id":104412772,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104150066/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104412772"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104412772, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5fdc370d03713bece2b09734f5b40258" } } $('.js-work-strip[data-work-id=104412772]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104412772,"title":"A systematic literature review on the applications of Bayesian networks to predict software quality","translated_title":"","metadata":{"publisher":"Springer Science and Business Media LLC","grobid_abstract":"Bayesian networks (BN) have been used for decision making in software engineering for many years. In other fields such as bioinformatics, BNs are rigorously evaluated in terms of the techniques that are used to build the network structure and to learn the parameters. We extend our prior mapping study to investigate the extent to which contextual and methodological details regarding BN construction are reported in the studies. We conduct a systematic literature review on the applications of BNs to predict software quality. We focus on more detailed questions regarding (1) dataset characteristics, (2) techniques used for parameter learning, (3) techniques used for structure learning, (4) use of tools, and (5) model validation techniques. Results on ten primary studies show that BNs are mostly built based on expert knowledge, i.e. structure and prior distributions are defined by experts, whereas authors benefit from BN tools and quantitative data to validate their models. In most of the papers, authors do not clearly explain their justification for choosing a specific technique, and they do not compare their proposed BNs with other machine learning approaches. There is also a lack of consensus on the performance measures to validate the proposed BNs. Compared to other domains, the use of BNs is still very limited and current publications do not report enough details to replicate the studies. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98025366"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98025366/Predictive_analytics_in_healthcare_epileptic_seizure_recognition"><img alt="Research paper thumbnail of Predictive analytics in healthcare epileptic seizure recognition" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98025366/Predictive_analytics_in_healthcare_epileptic_seizure_recognition">Predictive analytics in healthcare epileptic seizure recognition</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Introduction Clinical applications of electroencephalography (EEG) span a very broad range of dia...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Introduction Clinical applications of electroencephalography (EEG) span a very broad range of diagnostic conditions. Epileptic seizure is the fourth most common neurological disorder in that. Related Work There has been considerable progress in clinical understanding of epilepsy, however many aspects of seizure prevention are still a mystery. Predictive modeling of EEG can provide significant value addition to substantiate the diagnosis of epilepsy. Methodology Machine learning algorithms are applied to predict the probability of epileptic seizure using an open source multi-class dataset. Results and Discussion Comparing the F-score from different classifiers, it is found that XGBoost gives the best performance in binary classification and Random Forest provides the best performance in multinomial classification. Conclusion Our results show that it is possible to predict epileptic seizure with significant accuracy from non-epileptic parameters using a suitable machine learning algor...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98025366"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98025366"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98025366; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98025366]").text(description); $(".js-view-count[data-work-id=98025366]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98025366; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98025366']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 98025366, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=98025366]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":98025366,"title":"Predictive analytics in healthcare epileptic seizure recognition","translated_title":"","metadata":{"abstract":"Introduction Clinical applications of electroencephalography (EEG) span a very broad range of diagnostic conditions. Epileptic seizure is the fourth most common neurological disorder in that. Related Work There has been considerable progress in clinical understanding of epilepsy, however many aspects of seizure prevention are still a mystery. Predictive modeling of EEG can provide significant value addition to substantiate the diagnosis of epilepsy. Methodology Machine learning algorithms are applied to predict the probability of epileptic seizure using an open source multi-class dataset. Results and Discussion Comparing the F-score from different classifiers, it is found that XGBoost gives the best performance in binary classification and Random Forest provides the best performance in multinomial classification. Conclusion Our results show that it is possible to predict epileptic seizure with significant accuracy from non-epileptic parameters using a suitable machine learning algor...","publisher":"CASCON","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"Introduction Clinical applications of electroencephalography (EEG) span a very broad range of diagnostic conditions. Epileptic seizure is the fourth most common neurological disorder in that. Related Work There has been considerable progress in clinical understanding of epilepsy, however many aspects of seizure prevention are still a mystery. Predictive modeling of EEG can provide significant value addition to substantiate the diagnosis of epilepsy. Methodology Machine learning algorithms are applied to predict the probability of epileptic seizure using an open source multi-class dataset. Results and Discussion Comparing the F-score from different classifiers, it is found that XGBoost gives the best performance in binary classification and Random Forest provides the best performance in multinomial classification. Conclusion Our results show that it is possible to predict epileptic seizure with significant accuracy from non-epileptic parameters using a suitable machine learning algor...","internal_url":"https://www.academia.edu/98025366/Predictive_analytics_in_healthcare_epileptic_seizure_recognition","translated_internal_url":"","created_at":"2023-03-06T05:16:18.218-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Predictive_analytics_in_healthcare_epileptic_seizure_recognition","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":7648,"name":"Epilepsy","url":"https://www.academia.edu/Documents/in/Epilepsy"},{"id":10904,"name":"Electroencephalography","url":"https://www.academia.edu/Documents/in/Electroencephalography"},{"id":70995,"name":"Random Forest","url":"https://www.academia.edu/Documents/in/Random_Forest"},{"id":154214,"name":"Binary Classification","url":"https://www.academia.edu/Documents/in/Binary_Classification"}],"urls":[{"id":29535716,"url":"https://dl.acm.org/citation.cfm?id=3291327"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98002162"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98002162/An_Improvement_to_Test_Case_Failure_Prediction_in_the_Context_of_Test_Case_Prioritization"><img alt="Research paper thumbnail of An Improvement to Test Case Failure Prediction in the Context of Test Case Prioritization" class="work-thumbnail" src="https://attachments.academia-assets.com/99472653/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98002162/An_Improvement_to_Test_Case_Failure_Prediction_in_the_Context_of_Test_Case_Prioritization">An Improvement to Test Case Failure Prediction in the Context of Test Case Prioritization</a></div><div class="wp-workCard_item"><span>Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f205e7ee0c64e8edd7acd8c50d4959f6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":99472653,"asset_id":98002162,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/99472653/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98002162"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98002162"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98002162; 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Background: The process of prioritizing test cases aims to come up with a ranked test suite where test cases meeting certain criteria are prioritized. One criterion may be the ability of test cases to nd faults that can be predicted a priori. Ranking test cases and executing the top-ranked test cases is particularly benecial when projects have tight schedules and budgets. Method: We performed the comparison by rst rebuilding the predictive models using the features from the original study and then we extended the original work to improve the predictive models using new features by combining with the existing ones. Results: The results of our study, using a dataset of ve open-source systems, conrm that the ndings from the original study hold and that our predictive models with new features outperform the original models in predicting and prioritizing the failing test cases. 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Bu kavram de 搂i鲁iklik ba 搂la鲁m olarak tanmlanmaktadr. Bu 莽al鲁mada end眉striyel bir yazlm geli鲁tirme ortamnda de 搂i鲁iklik ba 搂la鲁m ile yazlm hatalarnn ili鲁kisi ara鲁trlm鲁tr.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Ulusal Yaz谋l谋m M眉hendisli臒i Sempozyumu","grobid_abstract_attachment_id":81696897},"translated_abstract":null,"internal_url":"https://www.academia.edu/73000119/De%C4%9Fi%C5%9Fiklik_Ba%C4%9Fla%C5%9F%C4%B1m%C4%B1_ve_Yaz%C4%B1l%C4%B1m_Hatalar%C4%B1_%C4%B0li%C5%9Fkisinin_%C4%B0ncelenmesi","translated_internal_url":"","created_at":"2022-03-04T00:19:26.257-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696897,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696897/thumbnails/1.jpg","file_name":"40_Bildiri.pdf","download_url":"https://www.academia.edu/attachments/81696897/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Degisiklik_Baglasimi_ve_Yazilim_Hatalari.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696897/40_Bildiri-libre.pdf?1646469772=\u0026response-content-disposition=attachment%3B+filename%3DDegisiklik_Baglasimi_ve_Yazilim_Hatalari.pdf\u0026Expires=1732998483\u0026Signature=fdYeTvoEuGAm6ACf28gu-auKsHEGIpCWY4ziWyhX9kCxJpU01IovfxcXBzTBaXalFCdX8dg2iA-UgYSHp7laQdt0s4zA-NnzX4jipKOqMotx5uTn9l-6F6IszZF~fCbmWTR6i60p~anRZqXixo7TIu7S0siGxESqE2SRRRBkrP2znwZEMriXA3Mu2P2d2a5og1bCjg-yKhjiIJJyFg3ZgUPvbc2WDLSUqvlFm3TjrytqquSWzMgydkpIhhPAQR-KvelQH~BtKFbLlLxBieJPznOFTHFHgBQ3i0NvzMOBDQOK03tckHEZSpVjrhwmybr4iaSZQv81H~CyHUj9QHHeqw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"De臒i艧iklik_Ba臒la艧谋m谋_ve_Yaz谋l谋m_Hatalar谋_陌li艧kisinin_陌ncelenmesi","translated_slug":"","page_count":12,"language":"tr","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696897,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696897/thumbnails/1.jpg","file_name":"40_Bildiri.pdf","download_url":"https://www.academia.edu/attachments/81696897/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Degisiklik_Baglasimi_ve_Yazilim_Hatalari.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696897/40_Bildiri-libre.pdf?1646469772=\u0026response-content-disposition=attachment%3B+filename%3DDegisiklik_Baglasimi_ve_Yazilim_Hatalari.pdf\u0026Expires=1732998483\u0026Signature=fdYeTvoEuGAm6ACf28gu-auKsHEGIpCWY4ziWyhX9kCxJpU01IovfxcXBzTBaXalFCdX8dg2iA-UgYSHp7laQdt0s4zA-NnzX4jipKOqMotx5uTn9l-6F6IszZF~fCbmWTR6i60p~anRZqXixo7TIu7S0siGxESqE2SRRRBkrP2znwZEMriXA3Mu2P2d2a5og1bCjg-yKhjiIJJyFg3ZgUPvbc2WDLSUqvlFm3TjrytqquSWzMgydkpIhhPAQR-KvelQH~BtKFbLlLxBieJPznOFTHFHgBQ3i0NvzMOBDQOK03tckHEZSpVjrhwmybr4iaSZQv81H~CyHUj9QHHeqw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"}],"urls":[]}, dispatcherData: dispatcherData }); 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This could lead to dynamic spectrum allocation methods to address potential spectrum shortages facing Internet of Things (IoT) deployments. Background: ANNs are a popular choice for spectrum prediction. Traditionally, ARIMA models have been at the forefront of forecasting and prediction but ANNs that learn from time series have demonstrated good performance using both simulated datasets and real-life data collected in the cellular bands. Methodology: We use three prediction models, a baseline which simply delays the time series, a seasonal ARIMA model and a TDNN. We test their performance on an hourly dataset in LMR bands collected in Ottawa, Canada between the dates of October 2016 and April 2017. Results: We demonstrate that TDNN yields improvements over seasonal ARIMA models in predicting short time horizons. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000117"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000117/The_Impact_of_Confirmation_Bias_on_the_Release_based_Defect_Prediction_of_Developer_Groups"><img alt="Research paper thumbnail of The Impact of Confirmation Bias on the Release-based Defect Prediction of Developer Groups" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000117/The_Impact_of_Confirmation_Bias_on_the_Release_based_Defect_Prediction_of_Developer_Groups">The Impact of Confirmation Bias on the Release-based Defect Prediction of Developer Groups</a></div><div class="wp-workCard_item"><span>International Conference on Software Engineering and Knowledge Engineering</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT During software development life cycle (SDLC), source codes are created and updated by g...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT During software development life cycle (SDLC), source codes are created and updated by groups of one or more developers. Information about the defect rates introduced by developer groups for the current release of a software product might guide project managers to form developer groups in order to manage defect rates for the next releases. In this research, we use partial least squares regression (PLSR) and principal component regression (PCR) to model the relation between defect rates and a specific cognitive aspect of developers, namely confirmation bias. In order to empirically estimate the performance of our model, we use datasets from three industrial software projects.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000117"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000117"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000117; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000117]").text(description); $(".js-view-count[data-work-id=73000117]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000117; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000117']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000117, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000117]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000117,"title":"The Impact of Confirmation Bias on the Release-based Defect Prediction of Developer Groups","translated_title":"","metadata":{"abstract":"ABSTRACT During software development life cycle (SDLC), source codes are created and updated by groups of one or more developers. Information about the defect rates introduced by developer groups for the current release of a software product might guide project managers to form developer groups in order to manage defect rates for the next releases. In this research, we use partial least squares regression (PLSR) and principal component regression (PCR) to model the relation between defect rates and a specific cognitive aspect of developers, namely confirmation bias. In order to empirically estimate the performance of our model, we use datasets from three industrial software projects.","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"International Conference on Software Engineering and Knowledge Engineering"},"translated_abstract":"ABSTRACT During software development life cycle (SDLC), source codes are created and updated by groups of one or more developers. Information about the defect rates introduced by developer groups for the current release of a software product might guide project managers to form developer groups in order to manage defect rates for the next releases. In this research, we use partial least squares regression (PLSR) and principal component regression (PCR) to model the relation between defect rates and a specific cognitive aspect of developers, namely confirmation bias. In order to empirically estimate the performance of our model, we use datasets from three industrial software projects.","internal_url":"https://www.academia.edu/73000117/The_Impact_of_Confirmation_Bias_on_the_Release_based_Defect_Prediction_of_Developer_Groups","translated_internal_url":"","created_at":"2022-03-04T00:19:25.463-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Impact_of_Confirmation_Bias_on_the_Release_based_Defect_Prediction_of_Developer_Groups","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000116"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000116/Predicting_Implantation_Outcome_from_Imbalanced_IVF_Dataset"><img alt="Research paper thumbnail of Predicting Implantation Outcome from Imbalanced IVF Dataset" class="work-thumbnail" src="https://attachments.academia-assets.com/83892387/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000116/Predicting_Implantation_Outcome_from_Imbalanced_IVF_Dataset">Predicting Implantation Outcome from Imbalanced IVF Dataset</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract-Predicting implantation outcomes of invitro fertilization (IVF) embryos is critical for ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract-Predicting implantation outcomes of invitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied Naive Bayes classifier to an original IVF dataset in order to discriminate embryos according to implantation potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of imbalance, we examined the effects of over sampling the minority class, under sampling the majority class and adjustment of the decision threshold on the classification performance. We have used features of Receiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve same prediction performance with less data as well as 736 embryo samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="553874e0991e1e1b57984ecc48c453f4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83892387,"asset_id":73000116,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83892387/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000116"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000116"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000116; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000116]").text(description); $(".js-view-count[data-work-id=73000116]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000116; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000116']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000116, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "553874e0991e1e1b57984ecc48c453f4" } } $('.js-work-strip[data-work-id=73000116]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000116,"title":"Predicting Implantation Outcome from Imbalanced IVF Dataset","translated_title":"","metadata":{"abstract":"Abstract-Predicting implantation outcomes of invitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied Naive Bayes classifier to an original IVF dataset in order to discriminate embryos according to implantation potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of imbalance, we examined the effects of over sampling the minority class, under sampling the majority class and adjustment of the decision threshold on the classification performance. We have used features of Receiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve same prediction performance with less data as well as 736 embryo samples.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"Abstract-Predicting implantation outcomes of invitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied Naive Bayes classifier to an original IVF dataset in order to discriminate embryos according to implantation potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of imbalance, we examined the effects of over sampling the minority class, under sampling the majority class and adjustment of the decision threshold on the classification performance. We have used features of Receiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve same prediction performance with less data as well as 736 embryo samples.","internal_url":"https://www.academia.edu/73000116/Predicting_Implantation_Outcome_from_Imbalanced_IVF_Dataset","translated_internal_url":"","created_at":"2022-03-04T00:19:25.200-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":83892387,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/83892387/thumbnails/1.jpg","file_name":"Predicting_Implantation_Outcome_from_Imb20220411-30673-lr27oq.pdf","download_url":"https://www.academia.edu/attachments/83892387/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predicting_Implantation_Outcome_from_Imb.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/83892387/Predicting_Implantation_Outcome_from_Imb20220411-30673-lr27oq.pdf?1649727377=\u0026response-content-disposition=attachment%3B+filename%3DPredicting_Implantation_Outcome_from_Imb.pdf\u0026Expires=1732998483\u0026Signature=BsyVC1HK0wo0pD~ehigft6SWegGvBBdtVKaa9zm5bvo83v37psktlKDhW3ZRbyJWD7X6lbYdQGh5vRw4j1uY~EkD89ZVm-Ag5ctiLueqaeqFeetKqM05rHWj~k02Ea5y0dfo3nreHcqzkDGPsRpzJVEe-f38hZqJ1cfjNOcIUDNe-u~Ndcob326TQRM4uO1O-tPzLHbzQO9XpTKFyWWYf7iQQBgBjDO9-YW3CD6M3NdDPE2uu5Mn4oi4TpQUGCXD6tsni250Tmkyrwy0S0XgbsBrid3fUknGq7ByBF0VM-EFZl6gHmMdUIX~5-xNqkWLTEFbbsL~vOXKSkOUTLbK4A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Predicting_Implantation_Outcome_from_Imbalanced_IVF_Dataset","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":83892387,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/83892387/thumbnails/1.jpg","file_name":"Predicting_Implantation_Outcome_from_Imb20220411-30673-lr27oq.pdf","download_url":"https://www.academia.edu/attachments/83892387/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predicting_Implantation_Outcome_from_Imb.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/83892387/Predicting_Implantation_Outcome_from_Imb20220411-30673-lr27oq.pdf?1649727377=\u0026response-content-disposition=attachment%3B+filename%3DPredicting_Implantation_Outcome_from_Imb.pdf\u0026Expires=1732998483\u0026Signature=BsyVC1HK0wo0pD~ehigft6SWegGvBBdtVKaa9zm5bvo83v37psktlKDhW3ZRbyJWD7X6lbYdQGh5vRw4j1uY~EkD89ZVm-Ag5ctiLueqaeqFeetKqM05rHWj~k02Ea5y0dfo3nreHcqzkDGPsRpzJVEe-f38hZqJ1cfjNOcIUDNe-u~Ndcob326TQRM4uO1O-tPzLHbzQO9XpTKFyWWYf7iQQBgBjDO9-YW3CD6M3NdDPE2uu5Mn4oi4TpQUGCXD6tsni250Tmkyrwy0S0XgbsBrid3fUknGq7ByBF0VM-EFZl6gHmMdUIX~5-xNqkWLTEFbbsL~vOXKSkOUTLbK4A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":18227790,"url":"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1068.3142\u0026rep=rep1\u0026type=pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000115"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000115/On_Empirically_Examining_The_Effectiveness_Of_Deep_Learning_Based_Bug_Localization_Models"><img alt="Research paper thumbnail of On Empirically Examining The Effectiveness Of Deep Learning-Based Bug Localization Models" class="work-thumbnail" src="https://attachments.academia-assets.com/81696894/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000115/On_Empirically_Examining_The_Effectiveness_Of_Deep_Learning_Based_Bug_Localization_Models">On Empirically Examining The Effectiveness Of Deep Learning-Based Bug Localization Models</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Software Bug Localization involves a significant amount of time and effort on the part of the sof...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Software Bug Localization involves a significant amount of time and effort on the part of the software developer. Many state-of-the-art bug localization models have been proposed in the past, to help developers localize bugs easily. However, none of these models meet the adoption thresholds of the software practitioner. Recently some deep learning-based models have been proposed, that have been shown to perform better than the state-of-the-art models. With this motivation, we experiment on Convolution Neural Networks (CNNs) to examine their effectiveness in localizing bugs. We also train a SimpleLogistic model as a baseline model for our experiments. We train both our models on five open source Java projects and compare their performance across the projects. Our experiments show that the CNN models perform better than the SimpleLogistic models in most of the cases, but do not meet the adoption criteria set by the practitioners.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="af4a4ad55aaa410820fb842c2d7e911d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696894,"asset_id":73000115,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696894/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000115"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000115"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000115; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000115]").text(description); $(".js-view-count[data-work-id=73000115]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000115; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000115']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000115, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "af4a4ad55aaa410820fb842c2d7e911d" } } $('.js-work-strip[data-work-id=73000115]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000115,"title":"On Empirically Examining The Effectiveness Of Deep Learning-Based Bug Localization Models","translated_title":"","metadata":{"abstract":"Software Bug Localization involves a significant amount of time and effort on the part of the software developer. Many state-of-the-art bug localization models have been proposed in the past, to help developers localize bugs easily. However, none of these models meet the adoption thresholds of the software practitioner. Recently some deep learning-based models have been proposed, that have been shown to perform better than the state-of-the-art models. With this motivation, we experiment on Convolution Neural Networks (CNNs) to examine their effectiveness in localizing bugs. We also train a SimpleLogistic model as a baseline model for our experiments. We train both our models on five open source Java projects and compare their performance across the projects. Our experiments show that the CNN models perform better than the SimpleLogistic models in most of the cases, but do not meet the adoption criteria set by the practitioners.","publisher":"Ryerson University Library and Archives"},"translated_abstract":"Software Bug Localization involves a significant amount of time and effort on the part of the software developer. Many state-of-the-art bug localization models have been proposed in the past, to help developers localize bugs easily. However, none of these models meet the adoption thresholds of the software practitioner. Recently some deep learning-based models have been proposed, that have been shown to perform better than the state-of-the-art models. With this motivation, we experiment on Convolution Neural Networks (CNNs) to examine their effectiveness in localizing bugs. We also train a SimpleLogistic model as a baseline model for our experiments. We train both our models on five open source Java projects and compare their performance across the projects. Our experiments show that the CNN models perform better than the SimpleLogistic models in most of the cases, but do not meet the adoption criteria set by the practitioners.","internal_url":"https://www.academia.edu/73000115/On_Empirically_Examining_The_Effectiveness_Of_Deep_Learning_Based_Bug_Localization_Models","translated_internal_url":"","created_at":"2022-03-04T00:19:25.007-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696894,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696894/thumbnails/1.jpg","file_name":"Sravya_Sravya.pdf","download_url":"https://www.academia.edu/attachments/81696894/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"On_Empirically_Examining_The_Effectivene.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696894/Sravya_Sravya-libre.pdf?1646469783=\u0026response-content-disposition=attachment%3B+filename%3DOn_Empirically_Examining_The_Effectivene.pdf\u0026Expires=1732998483\u0026Signature=HZ-OM4VHCnA6vX5f6tqszCevxbEAVXIwngriggBKR8FlG6BdG3e73paLtI0pHr2E2Z91DaeI0pHg17ICZEN8gsbjOootcfdQoWmNXPR-jKcJbUAcKmDyzM7gChrtBv1isM7pueNKGV7CCbRetNvQZwGZjs1jm38PHJLrVfAJH7jXpa5v5xKCa-VUEAxXmp9vtx~SXZHcKWc-mt3uVdYX0U6qV-Hl21mtYPgARvKByQCBqFo~Kx0yWi8PcfUiak0CpW2sd3bKy7Vx-0L4Zklk34vnVXGEPuVxPLA1Gb2ewnpHXS3HXIR8edElM1H1vHnuAtqTjnfcRMU8CeDp9KZ-rQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"On_Empirically_Examining_The_Effectiveness_Of_Deep_Learning_Based_Bug_Localization_Models","translated_slug":"","page_count":160,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696894,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696894/thumbnails/1.jpg","file_name":"Sravya_Sravya.pdf","download_url":"https://www.academia.edu/attachments/81696894/download_file?st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"On_Empirically_Examining_The_Effectivene.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696894/Sravya_Sravya-libre.pdf?1646469783=\u0026response-content-disposition=attachment%3B+filename%3DOn_Empirically_Examining_The_Effectivene.pdf\u0026Expires=1732998483\u0026Signature=HZ-OM4VHCnA6vX5f6tqszCevxbEAVXIwngriggBKR8FlG6BdG3e73paLtI0pHr2E2Z91DaeI0pHg17ICZEN8gsbjOootcfdQoWmNXPR-jKcJbUAcKmDyzM7gChrtBv1isM7pueNKGV7CCbRetNvQZwGZjs1jm38PHJLrVfAJH7jXpa5v5xKCa-VUEAxXmp9vtx~SXZHcKWc-mt3uVdYX0U6qV-Hl21mtYPgARvKByQCBqFo~Kx0yWi8PcfUiak0CpW2sd3bKy7Vx-0L4Zklk34vnVXGEPuVxPLA1Gb2ewnpHXS3HXIR8edElM1H1vHnuAtqTjnfcRMU8CeDp9KZ-rQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000114"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000114/Risk_Parity_Models_for_Portfolio_Optimization_A_Study_of_the_Toronto_Stock_Exchange"><img alt="Research paper thumbnail of Risk Parity Models for Portfolio Optimization: A Study of the Toronto Stock Exchange" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000114/Risk_Parity_Models_for_Portfolio_Optimization_A_Study_of_the_Toronto_Stock_Exchange">Risk Parity Models for Portfolio Optimization: A Study of the Toronto Stock Exchange</a></div><div class="wp-workCard_item"><span>2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It has been more than 60 years since the development of Mean-Variance (MV) framework and inceptio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It has been more than 60 years since the development of Mean-Variance (MV) framework and inception of Modern Portfolio theory. Despite its wide acceptance and applicability, it suffers from few limitations. This paper addresses two issues of MV framework: (i) estimation errors of mean-variance model, and (ii) instability of covariance matrix. Risk parity models, robust statistics and robust optimization minimize the effects of estimation errors of parameters of MV framework. The paper presents two such risk parity models for portfolio optimization, namely, (a) Hierarchical Risk Parity model based on Historical correlation (HRP-HC), and (b) Hierarchical Risk parity model based on Gerber statistics (HRP-GS). The models are tested and analysed using stocks comprising the TSX complete index for a time period of 10 years ranging from 2007 to 2016. Results suggest that the proposed HRP-GS model outperforms HRP-HC model. This is due to the fact that the HRP-GS model integrates the advantages of a risk parity model (i.e. HRP) and robust statistics (i.e. Gerber statistics).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000114"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000114"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000114; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000114]").text(description); $(".js-view-count[data-work-id=73000114]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000114; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000114']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000114, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000114]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000114,"title":"Risk Parity Models for Portfolio Optimization: A Study of the Toronto Stock Exchange","translated_title":"","metadata":{"abstract":"It has been more than 60 years since the development of Mean-Variance (MV) framework and inception of Modern Portfolio theory. 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This is due to the fact that the HRP-GS model integrates the advantages of a risk parity model (i.e. HRP) and robust statistics (i.e. Gerber statistics).","publisher":"IEEE","publication_name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)"},"translated_abstract":"It has been more than 60 years since the development of Mean-Variance (MV) framework and inception of Modern Portfolio theory. Despite its wide acceptance and applicability, it suffers from few limitations. This paper addresses two issues of MV framework: (i) estimation errors of mean-variance model, and (ii) instability of covariance matrix. Risk parity models, robust statistics and robust optimization minimize the effects of estimation errors of parameters of MV framework. The paper presents two such risk parity models for portfolio optimization, namely, (a) Hierarchical Risk Parity model based on Historical correlation (HRP-HC), and (b) Hierarchical Risk parity model based on Gerber statistics (HRP-GS). The models are tested and analysed using stocks comprising the TSX complete index for a time period of 10 years ranging from 2007 to 2016. Results suggest that the proposed HRP-GS model outperforms HRP-HC model. This is due to the fact that the HRP-GS model integrates the advantages of a risk parity model (i.e. HRP) and robust statistics (i.e. Gerber statistics).","internal_url":"https://www.academia.edu/73000114/Risk_Parity_Models_for_Portfolio_Optimization_A_Study_of_the_Toronto_Stock_Exchange","translated_internal_url":"","created_at":"2022-03-04T00:19:24.712-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Risk_Parity_Models_for_Portfolio_Optimization_A_Study_of_the_Toronto_Stock_Exchange","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[],"urls":[{"id":18227789,"url":"http://xplorestaging.ieee.org/ielx7/8870906/8876888/08876902.pdf?arnumber=8876902"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000111"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000111/Handling_the_Imbalance_Problem_of_IVF_Implantation_Prediction"><img alt="Research paper thumbnail of Handling the Imbalance Problem of IVF Implantation Prediction" class="work-thumbnail" src="https://attachments.academia-assets.com/81696801/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000111/Handling_the_Imbalance_Problem_of_IVF_Implantation_Prediction">Handling the Imbalance Problem of IVF Implantation Prediction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Predicting implantation outcomes of in- vitro fertilization (IVF) embryos is critical for the suc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Predicting implantation outcomes of in- vitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied the Naive Bayes classifler to an original IVF dataset in order to discriminate embryos according to the implanta- tion potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of im- balance, we examined the efiects of oversampling the minority class, undersampling the majority class and the adjustment of the decision threshold on the clas- siflcation performance. We have used features of Re- ceiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve the same prediction performance with less data as well as 736 embryo samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="add1d825be06dabce328d434299816ea" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696801,"asset_id":73000111,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696801/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000111"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000111"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000111; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000111]").text(description); $(".js-view-count[data-work-id=73000111]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000111; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000111']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000111, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "add1d825be06dabce328d434299816ea" } } $('.js-work-strip[data-work-id=73000111]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000111,"title":"Handling the Imbalance Problem of IVF Implantation Prediction","translated_title":"","metadata":{"abstract":"Predicting implantation outcomes of in- vitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied the Naive Bayes classifler to an original IVF dataset in order to discriminate embryos according to the implanta- tion potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of im- balance, we examined the efiects of oversampling the minority class, undersampling the majority class and the adjustment of the decision threshold on the clas- siflcation performance. We have used features of Re- ceiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve the same prediction performance with less data as well as 736 embryo samples.","publication_date":{"day":null,"month":null,"year":2010,"errors":{}}},"translated_abstract":"Predicting implantation outcomes of in- vitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied the Naive Bayes classifler to an original IVF dataset in order to discriminate embryos according to the implanta- tion potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of im- balance, we examined the efiects of oversampling the minority class, undersampling the majority class and the adjustment of the decision threshold on the clas- siflcation performance. We have used features of Re- ceiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve the same prediction performance with less data as well as 736 embryo samples.","internal_url":"https://www.academia.edu/73000111/Handling_the_Imbalance_Problem_of_IVF_Implantation_Prediction","translated_internal_url":"","created_at":"2022-03-04T00:19:23.704-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696801,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696801/thumbnails/1.jpg","file_name":"IJCS_37_2_06.pdf","download_url":"https://www.academia.edu/attachments/81696801/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Handling_the_Imbalance_Problem_of_IVF_Im.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696801/IJCS_37_2_06-libre.pdf?1646469777=\u0026response-content-disposition=attachment%3B+filename%3DHandling_the_Imbalance_Problem_of_IVF_Im.pdf\u0026Expires=1732998484\u0026Signature=IXoRnVIz-icxEm1sI5kGDNUUSrpopxQw9vlGAFgyZJ7rgjg5chsIDRwzNxLnOw3PIqdn6UHuWnNZzeRSsX8okG8bXYUh6zdb8KWtWzQbjNV6glovMrTdm2x1iN6qMTtCmOoW~Z6YJMs~8xThrbBSh-m2BY2J4huFvf856WK8QvB3HSMlT8bw~2xz3PxMrewrUOqB7dCcnQF3fYNKqH143e3RbjtGD-oReL0K9rPNQMjWs0yhp6lHhfd-gAbX-CDMhvh~~cYe5VMnuj0YrnTFVZZ5CNtY7WYlGDP1dLpP8NsvrVggPTqU-WmN0fDnGHzGhXwO6TOkP9oEWBJPlI5tRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Handling_the_Imbalance_Problem_of_IVF_Implantation_Prediction","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696801,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696801/thumbnails/1.jpg","file_name":"IJCS_37_2_06.pdf","download_url":"https://www.academia.edu/attachments/81696801/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Handling_the_Imbalance_Problem_of_IVF_Im.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696801/IJCS_37_2_06-libre.pdf?1646469777=\u0026response-content-disposition=attachment%3B+filename%3DHandling_the_Imbalance_Problem_of_IVF_Im.pdf\u0026Expires=1732998484\u0026Signature=IXoRnVIz-icxEm1sI5kGDNUUSrpopxQw9vlGAFgyZJ7rgjg5chsIDRwzNxLnOw3PIqdn6UHuWnNZzeRSsX8okG8bXYUh6zdb8KWtWzQbjNV6glovMrTdm2x1iN6qMTtCmOoW~Z6YJMs~8xThrbBSh-m2BY2J4huFvf856WK8QvB3HSMlT8bw~2xz3PxMrewrUOqB7dCcnQF3fYNKqH143e3RbjtGD-oReL0K9rPNQMjWs0yhp6lHhfd-gAbX-CDMhvh~~cYe5VMnuj0YrnTFVZZ5CNtY7WYlGDP1dLpP8NsvrVggPTqU-WmN0fDnGHzGhXwO6TOkP9oEWBJPlI5tRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"}],"urls":[{"id":18227787,"url":"http://www.iaeng.org/IJCS/issues_v37/issue_2/IJCS_37_2_06.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000110"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000110/Predicting_Commentaries_on_a_Financial_Report_with_Recurrent_Neural_Networks"><img alt="Research paper thumbnail of Predicting Commentaries on a Financial Report with Recurrent Neural Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000110/Predicting_Commentaries_on_a_Financial_Report_with_Recurrent_Neural_Networks">Predicting Commentaries on a Financial Report with Recurrent Neural Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Aim: The paper aims to automatically generate commentaries on financial reports. Background: Anal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Aim: The paper aims to automatically generate commentaries on financial reports. Background: Analysing and commenting financial reports is critical to evaluate the performance of a company so that management may change course to meet the targets. Generating commentaries is a task that relies on the expertise of analysts. Methodology: We propose a encoder-decoder architecture based on Recurrent Neural Networks (RNN) that are trained on both financial reports and commentaries. This architecture learns to generate those commentaries from the detected patterns on data. The proposed model is assessed on both synthetic and real data. We compare different neural network combinations on both encoder and decoder, namely GRU, LSTM and one layer neural networks. Results: The accuracy of the generated commentaries is evaluated using BLEU, ROUGE and METEOR scores and probability of commentary generation. The results show that a combination of one layer neural network and an LSTM as encoder and d...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000110"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000110"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000110; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000110]").text(description); $(".js-view-count[data-work-id=73000110]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000110; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000110']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000110, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000110]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000110,"title":"Predicting Commentaries on a Financial Report with Recurrent Neural Networks","translated_title":"","metadata":{"abstract":"Aim: The paper aims to automatically generate commentaries on financial reports. Background: Analysing and commenting financial reports is critical to evaluate the performance of a company so that management may change course to meet the targets. Generating commentaries is a task that relies on the expertise of analysts. Methodology: We propose a encoder-decoder architecture based on Recurrent Neural Networks (RNN) that are trained on both financial reports and commentaries. This architecture learns to generate those commentaries from the detected patterns on data. The proposed model is assessed on both synthetic and real data. We compare different neural network combinations on both encoder and decoder, namely GRU, LSTM and one layer neural networks. Results: The accuracy of the generated commentaries is evaluated using BLEU, ROUGE and METEOR scores and probability of commentary generation. The results show that a combination of one layer neural network and an LSTM as encoder and d...","publisher":"Canadian Conference on AI","publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":"Aim: The paper aims to automatically generate commentaries on financial reports. Background: Analysing and commenting financial reports is critical to evaluate the performance of a company so that management may change course to meet the targets. Generating commentaries is a task that relies on the expertise of analysts. Methodology: We propose a encoder-decoder architecture based on Recurrent Neural Networks (RNN) that are trained on both financial reports and commentaries. This architecture learns to generate those commentaries from the detected patterns on data. The proposed model is assessed on both synthetic and real data. We compare different neural network combinations on both encoder and decoder, namely GRU, LSTM and one layer neural networks. 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The results show that a combination of one layer neural network and an LSTM as encoder and d...","internal_url":"https://www.academia.edu/73000110/Predicting_Commentaries_on_a_Financial_Report_with_Recurrent_Neural_Networks","translated_internal_url":"","created_at":"2022-03-04T00:19:23.436-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Predicting_Commentaries_on_a_Financial_Report_with_Recurrent_Neural_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":18227786,"url":"https://johnmaidens.com/papers/2019CanadianAI.pdf"}]}, dispatcherData: dispatcherData }); 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We aim at identifying, debating about, and mitigating the barriers challenging the design and execution of empirical studies in industrial settings. In the past workshops, we also aimed at improved understanding of the emergence of industrial-strength empirical results and the critical characteristics of the research methods needed to yield those results as well as aggregating individual studies' results towards practical, evidence-based guidelines. This year, we would like to give a special emphasis on building and managing big data systems and the use and benefit of empirical studies in this context. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000108"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000108/Confirmation_Bias_in_Software_Development_and_Testing_An_Analysis_of_the_Effects_of_Company_Size_Experience_and_Reasoning_Skills"><img alt="Research paper thumbnail of Confirmation Bias in Software Development and Testing: An Analysis of the Effects of Company Size, Experience and Reasoning Skills" class="work-thumbnail" src="https://attachments.academia-assets.com/81696797/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000108/Confirmation_Bias_in_Software_Development_and_Testing_An_Analysis_of_the_Effects_of_Company_Size_Experience_and_Reasoning_Skills">Confirmation Bias in Software Development and Testing: An Analysis of the Effects of Company Size, Experience and Reasoning Skills</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">During all levels of software testing, the goal should be to fail the code to discover software d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">During all levels of software testing, the goal should be to fail the code to discover software defects and hence to increase software quality. However, software developers and testers are more likely to choose positive tests rather than negative ones. This is due to the phenomenon called confirmation bias which is defined as the tendency to verify one鈥檚 own hypotheses rather than trying to refute them. In this work, we aimed at identifying the factors that may affect confirmation bias levels of software developers and testers. We have investigated the effects of company size, experience and reasoning skills on bias levels. We prepared pen-and-paper and interactive tests based on two tasks from cognitive psychology literature. During pen-and-paper test, subjects had to test given hypotheses, whereas interactive test required both hypotheses generation and testing. These tests were conducted on employees of one large scale telecommunications company, three small and medium scale soft...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d7a34274dd695eeaad8f9bbd04bb6178" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696797,"asset_id":73000108,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696797/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000108"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000108"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000108; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000108]").text(description); $(".js-view-count[data-work-id=73000108]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000108; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000108']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000108, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d7a34274dd695eeaad8f9bbd04bb6178" } } $('.js-work-strip[data-work-id=73000108]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000108,"title":"Confirmation Bias in Software Development and Testing: An Analysis of the Effects of Company Size, Experience and Reasoning Skills","translated_title":"","metadata":{"abstract":"During all levels of software testing, the goal should be to fail the code to discover software defects and hence to increase software quality. However, software developers and testers are more likely to choose positive tests rather than negative ones. This is due to the phenomenon called confirmation bias which is defined as the tendency to verify one鈥檚 own hypotheses rather than trying to refute them. In this work, we aimed at identifying the factors that may affect confirmation bias levels of software developers and testers. We have investigated the effects of company size, experience and reasoning skills on bias levels. We prepared pen-and-paper and interactive tests based on two tasks from cognitive psychology literature. During pen-and-paper test, subjects had to test given hypotheses, whereas interactive test required both hypotheses generation and testing. These tests were conducted on employees of one large scale telecommunications company, three small and medium scale soft...","publisher":"PPIG","publication_date":{"day":null,"month":null,"year":2010,"errors":{}}},"translated_abstract":"During all levels of software testing, the goal should be to fail the code to discover software defects and hence to increase software quality. However, software developers and testers are more likely to choose positive tests rather than negative ones. This is due to the phenomenon called confirmation bias which is defined as the tendency to verify one鈥檚 own hypotheses rather than trying to refute them. In this work, we aimed at identifying the factors that may affect confirmation bias levels of software developers and testers. We have investigated the effects of company size, experience and reasoning skills on bias levels. We prepared pen-and-paper and interactive tests based on two tasks from cognitive psychology literature. During pen-and-paper test, subjects had to test given hypotheses, whereas interactive test required both hypotheses generation and testing. These tests were conducted on employees of one large scale telecommunications company, three small and medium scale soft...","internal_url":"https://www.academia.edu/73000108/Confirmation_Bias_in_Software_Development_and_Testing_An_Analysis_of_the_Effects_of_Company_Size_Experience_and_Reasoning_Skills","translated_internal_url":"","created_at":"2022-03-04T00:19:22.757-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696797,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696797/thumbnails/1.jpg","file_name":"PPIG2010.pdf","download_url":"https://www.academia.edu/attachments/81696797/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Confirmation_Bias_in_Software_Developmen.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696797/PPIG2010-libre.pdf?1646469779=\u0026response-content-disposition=attachment%3B+filename%3DConfirmation_Bias_in_Software_Developmen.pdf\u0026Expires=1732998484\u0026Signature=R6UwxV3frmkfGk1PqlzZbU776DRADG0bX0fIoQzoG4u2iz-3ckZx18mW1AQ14xFlCgYWfzs0xGC9gneYEaF2ngMc7aeI15wzctmLd90UTxkIbL~0ylTC4FenRSCJMQG-ANWhY4hVmI7CU8eoS6EGW4QYBEZABBi5PDLOZkPAZRMW4e3nOIviUoRmsjPvyPy4h~q6NUyaPRTiPr7C3pNfmMzImMGh7TiunAHaEWxmzlvHX7PCLKq1v6Y4YioZTasbDb58MJ2aWnjVNvxR0qQhTOkb2EDOg6I8XWIeUQ965DRRFXhHGnW8bn0bjbMy~kpxS3lGNbuBOVcy6jgrJTr1zA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Confirmation_Bias_in_Software_Development_and_Testing_An_Analysis_of_the_Effects_of_Company_Size_Experience_and_Reasoning_Skills","translated_slug":"","page_count":16,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696797,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696797/thumbnails/1.jpg","file_name":"PPIG2010.pdf","download_url":"https://www.academia.edu/attachments/81696797/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Confirmation_Bias_in_Software_Developmen.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696797/PPIG2010-libre.pdf?1646469779=\u0026response-content-disposition=attachment%3B+filename%3DConfirmation_Bias_in_Software_Developmen.pdf\u0026Expires=1732998484\u0026Signature=R6UwxV3frmkfGk1PqlzZbU776DRADG0bX0fIoQzoG4u2iz-3ckZx18mW1AQ14xFlCgYWfzs0xGC9gneYEaF2ngMc7aeI15wzctmLd90UTxkIbL~0ylTC4FenRSCJMQG-ANWhY4hVmI7CU8eoS6EGW4QYBEZABBi5PDLOZkPAZRMW4e3nOIviUoRmsjPvyPy4h~q6NUyaPRTiPr7C3pNfmMzImMGh7TiunAHaEWxmzlvHX7PCLKq1v6Y4YioZTasbDb58MJ2aWnjVNvxR0qQhTOkb2EDOg6I8XWIeUQ965DRRFXhHGnW8bn0bjbMy~kpxS3lGNbuBOVcy6jgrJTr1zA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":4761,"name":"Software Testing","url":"https://www.academia.edu/Documents/in/Software_Testing"},{"id":135988,"name":"Confirmation bias","url":"https://www.academia.edu/Documents/in/Confirmation_bias"}],"urls":[{"id":18227784,"url":"http://oro.open.ac.uk/45370/1/PPIG2010.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000107"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000107/Part_II_Evaluation_Field_Studies"><img alt="Research paper thumbnail of Part II. Evaluation: Field Studies" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000107/Part_II_Evaluation_Field_Studies">Part II. Evaluation: Field Studies</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One way to implement and evaluate the effectiveness of recommendation systems for software engine...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One way to implement and evaluate the effectiveness of recommendation systems for software engineering is to conduct field studies. Field studies are important as they are the extension of the laboratory experiments into real life situations of organizations and/or society. They bring greater realism to the phenomenon that is under study. However, field studies require following a rigorous research approach with many challenges attached, such as difficulties in implementing the research design, achieving sufficient control, replication, validity and reliability. In practice, another challenge is to find organizations who are prepared to be experimented on. In this chapter, we provide details regarding step-by-step process in the construction and deployment of recommendation systems for software engineering in the field. We also emphasize three main challenges (organizational, data, design) encountered during field studies in both general and specific to software organizations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000107"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000107"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000107; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000107]").text(description); $(".js-view-count[data-work-id=73000107]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000107; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000107']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000107, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000107]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000107,"title":"Part II. Evaluation: Field Studies","translated_title":"","metadata":{"abstract":"One way to implement and evaluate the effectiveness of recommendation systems for software engineering is to conduct field studies. Field studies are important as they are the extension of the laboratory experiments into real life situations of organizations and/or society. They bring greater realism to the phenomenon that is under study. However, field studies require following a rigorous research approach with many challenges attached, such as difficulties in implementing the research design, achieving sufficient control, replication, validity and reliability. In practice, another challenge is to find organizations who are prepared to be experimented on. In this chapter, we provide details regarding step-by-step process in the construction and deployment of recommendation systems for software engineering in the field. We also emphasize three main challenges (organizational, data, design) encountered during field studies in both general and specific to software organizations.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}}},"translated_abstract":"One way to implement and evaluate the effectiveness of recommendation systems for software engineering is to conduct field studies. Field studies are important as they are the extension of the laboratory experiments into real life situations of organizations and/or society. They bring greater realism to the phenomenon that is under study. However, field studies require following a rigorous research approach with many challenges attached, such as difficulties in implementing the research design, achieving sufficient control, replication, validity and reliability. In practice, another challenge is to find organizations who are prepared to be experimented on. In this chapter, we provide details regarding step-by-step process in the construction and deployment of recommendation systems for software engineering in the field. We also emphasize three main challenges (organizational, data, design) encountered during field studies in both general and specific to software organizations.","internal_url":"https://www.academia.edu/73000107/Part_II_Evaluation_Field_Studies","translated_internal_url":"","created_at":"2022-03-04T00:19:22.551-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Part_II_Evaluation_Field_Studies","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000105"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000105/Applications_of_Feature_Selection_Techniques_on_Large_Biomedical_Datasets"><img alt="Research paper thumbnail of Applications of Feature Selection Techniques on Large Biomedical Datasets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000105/Applications_of_Feature_Selection_Techniques_on_Large_Biomedical_Datasets">Applications of Feature Selection Techniques on Large Biomedical Datasets</a></div><div class="wp-workCard_item"><span>Advances in Artificial Intelligence</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The main goal of this paper is to determine the best feature selection algorithm to use on large ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The main goal of this paper is to determine the best feature selection algorithm to use on large biomedical datasets. Feature Selection shows a potential role in analyzing large biomedical datasets. Four different feature selection techniques have been employed on large biomedical datasets. These techniques were Information Gain, Chi-Squared, Markov Blanket Discovery, and Recursive Feature Elimination. We measured the efficiency of the selection, the stability of the algorithms, and the quality of the features chosen. Of the four techniques used, the Information Gain and Chi-Squared filters were the most efficient and stable. Both Markov Blanket Discovery and Recursive Feature Elimination took significantly longer to select features, and were less stable. The features selected by Recursive Feature Elimination were of the highest quality, followed by Information Gain and Chi-Squared, and Markov Blanket Discovery placed last. For the purpose of education (e.g. those in the biomedical field teaching data techniques), we recommend Information Gain or Chi-Squared filter. For the purpose of research or analyzing, we recommend one of the filters or Recursive Feature Elimination, depending on the situation. We do not recommend the use of Markov Blanket discovery for the situations used in this trial, keeping in mind that the experiments were not exhaustive.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000105"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000105"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000105; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000105]").text(description); $(".js-view-count[data-work-id=73000105]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000105; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000105']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000105, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000105]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000105,"title":"Applications of Feature Selection Techniques on Large Biomedical Datasets","translated_title":"","metadata":{"abstract":"The main goal of this paper is to determine the best feature selection algorithm to use on large biomedical datasets. Feature Selection shows a potential role in analyzing large biomedical datasets. Four different feature selection techniques have been employed on large biomedical datasets. These techniques were Information Gain, Chi-Squared, Markov Blanket Discovery, and Recursive Feature Elimination. We measured the efficiency of the selection, the stability of the algorithms, and the quality of the features chosen. Of the four techniques used, the Information Gain and Chi-Squared filters were the most efficient and stable. Both Markov Blanket Discovery and Recursive Feature Elimination took significantly longer to select features, and were less stable. The features selected by Recursive Feature Elimination were of the highest quality, followed by Information Gain and Chi-Squared, and Markov Blanket Discovery placed last. For the purpose of education (e.g. those in the biomedical field teaching data techniques), we recommend Information Gain or Chi-Squared filter. For the purpose of research or analyzing, we recommend one of the filters or Recursive Feature Elimination, depending on the situation. We do not recommend the use of Markov Blanket discovery for the situations used in this trial, keeping in mind that the experiments were not exhaustive.","publisher":"Springer International Publishing","publication_name":"Advances in Artificial Intelligence"},"translated_abstract":"The main goal of this paper is to determine the best feature selection algorithm to use on large biomedical datasets. Feature Selection shows a potential role in analyzing large biomedical datasets. Four different feature selection techniques have been employed on large biomedical datasets. These techniques were Information Gain, Chi-Squared, Markov Blanket Discovery, and Recursive Feature Elimination. We measured the efficiency of the selection, the stability of the algorithms, and the quality of the features chosen. Of the four techniques used, the Information Gain and Chi-Squared filters were the most efficient and stable. Both Markov Blanket Discovery and Recursive Feature Elimination took significantly longer to select features, and were less stable. The features selected by Recursive Feature Elimination were of the highest quality, followed by Information Gain and Chi-Squared, and Markov Blanket Discovery placed last. For the purpose of education (e.g. those in the biomedical field teaching data techniques), we recommend Information Gain or Chi-Squared filter. For the purpose of research or analyzing, we recommend one of the filters or Recursive Feature Elimination, depending on the situation. We do not recommend the use of Markov Blanket discovery for the situations used in this trial, keeping in mind that the experiments were not exhaustive.","internal_url":"https://www.academia.edu/73000105/Applications_of_Feature_Selection_Techniques_on_Large_Biomedical_Datasets","translated_internal_url":"","created_at":"2022-03-04T00:19:22.298-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Applications_of_Feature_Selection_Techniques_on_Large_Biomedical_Datasets","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"}],"urls":[{"id":18227783,"url":"http://link.springer.com/content/pdf/10.1007/978-3-030-18305-9_57"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000104"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000104/Deep_Super_Learner_A_Deep_Ensemble_for_Classification_Problems"><img alt="Research paper thumbnail of Deep Super Learner: A Deep Ensemble for Classification Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/81696794/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000104/Deep_Super_Learner_A_Deep_Ensemble_for_Classification_Problems">Deep Super Learner: A Deep Ensemble for Classification Problems</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="da3d75afd9dee068b8040582959f6c06" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696794,"asset_id":73000104,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696794/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000104"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000104"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000104; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000104]").text(description); $(".js-view-count[data-work-id=73000104]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000104; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000104']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000104, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "da3d75afd9dee068b8040582959f6c06" } } $('.js-work-strip[data-work-id=73000104]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000104,"title":"Deep Super Learner: A Deep Ensemble for Classification Problems","translated_title":"","metadata":{"publisher":"Springer International Publishing","grobid_abstract":"Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datasets. While traditional machine learning algorithms can address these drawbacks, they are not typically capable of the performance levels achieved by deep neural networks. To improve performance, ensemble methods are used to combine multiple base learners. Super learning is an ensemble that finds the optimal combination of diverse learning algorithms. This paper proposes deep super learning as an approach which achieves log loss and accuracy results competitive to deep neural networks while employing traditional machine learning algorithms in a hierarchical structure. The deep super learner is flexible, adaptable, and easy to train with good performance across different tasks using identical hyper-parameter values. Using traditional machine learning requires fewer hyper-parameters, allows transparency into results, and has relatively fast convergence on smaller datasets. Experimental results show that the deep super learner has superior performance compared to the individual base learners, single-layer ensembles, and in some cases deep neural networks. Performance of the deep super learner may further be improved with task-specific tuning.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Lecture Notes in Computer Science","grobid_abstract_attachment_id":81696794},"translated_abstract":null,"internal_url":"https://www.academia.edu/73000104/Deep_Super_Learner_A_Deep_Ensemble_for_Classification_Problems","translated_internal_url":"","created_at":"2022-03-04T00:19:22.047-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696794,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696794/thumbnails/1.jpg","file_name":"1803.pdf","download_url":"https://www.academia.edu/attachments/81696794/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Deep_Super_Learner_A_Deep_Ensemble_for_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696794/1803-libre.pdf?1646469781=\u0026response-content-disposition=attachment%3B+filename%3DDeep_Super_Learner_A_Deep_Ensemble_for_C.pdf\u0026Expires=1732998484\u0026Signature=WJosHw098afbqssCxTHKW5oml8SMJaVvMi6axWg-Uo8NylwsvBEZPAJMUTCAa6gR6V6MPvl3lC-2ueFSiPFBFKtlUiZLXeLZi70lVaDTKNS04K1RtRbMWNKMf5EqqBcqwU3mBHG6q5k65Y4oYnzGcsO3wvupEWrv1nrdKqgOUTNEH2P9lKvT0j1GYVF3tQHI7i-UJUvunFHzNtEoiJMdflMUWQH0xK4KqR5R9Zw22yVVJruvsAtKqvgSBBJipoJ2JscrbNfUKmKew9QbuDTa2bhzu3oVM0Y7Zt7rYSrKQYDicdrMB7lvcIet3eEf0GLSg~9vOJsvvRzuKkVAu0GU3g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Deep_Super_Learner_A_Deep_Ensemble_for_Classification_Problems","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696794,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696794/thumbnails/1.jpg","file_name":"1803.pdf","download_url":"https://www.academia.edu/attachments/81696794/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Deep_Super_Learner_A_Deep_Ensemble_for_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696794/1803-libre.pdf?1646469781=\u0026response-content-disposition=attachment%3B+filename%3DDeep_Super_Learner_A_Deep_Ensemble_for_C.pdf\u0026Expires=1732998484\u0026Signature=WJosHw098afbqssCxTHKW5oml8SMJaVvMi6axWg-Uo8NylwsvBEZPAJMUTCAa6gR6V6MPvl3lC-2ueFSiPFBFKtlUiZLXeLZi70lVaDTKNS04K1RtRbMWNKMf5EqqBcqwU3mBHG6q5k65Y4oYnzGcsO3wvupEWrv1nrdKqgOUTNEH2P9lKvT0j1GYVF3tQHI7i-UJUvunFHzNtEoiJMdflMUWQH0xK4KqR5R9Zw22yVVJruvsAtKqvgSBBJipoJ2JscrbNfUKmKew9QbuDTa2bhzu3oVM0Y7Zt7rYSrKQYDicdrMB7lvcIet3eEf0GLSg~9vOJsvvRzuKkVAu0GU3g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":81696795,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696795/thumbnails/1.jpg","file_name":"1803.pdf","download_url":"https://www.academia.edu/attachments/81696795/download_file","bulk_download_file_name":"Deep_Super_Learner_A_Deep_Ensemble_for_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696795/1803-libre.pdf?1646469780=\u0026response-content-disposition=attachment%3B+filename%3DDeep_Super_Learner_A_Deep_Ensemble_for_C.pdf\u0026Expires=1732998484\u0026Signature=AUFRCOCw~ROasDBsdOsZCQefF3OBXJDje~EDzcDIumulMYwtbET6BgHH5hW7jN3TTul1kQjERF9w7wkUQ8edUr-PkBq-X6vFN~~G-r67ULbChnLzwuN4D3zWLrnugiiqmO~ZwZy-jXdwBMOEBqB2LJNoio5EyfXaL6FOGcRBI7yQnEX925h2TdGxFXhm76O00~aiES7wLGeQCGgGgRkJgD0h2DO1w7GMNSFDT6LfgarUuPAQ0ANL6UZo3OWuFNEIrzA1P3HSqsEZ~ct0QwYxPqufRJMBtVhNg~5XbDSiRvz2CkU~EM1Pf24J1bI7VaKU6WngpSQp8TCcUNy-3SQg~A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":18227782,"url":"http://arxiv.org/pdf/1803.02323"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000103"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000103/AI_Based_Software_Defect_Predictors_Applications_and_Benefits_in_a_Case_Study"><img alt="Research paper thumbnail of AI-Based Software Defect Predictors: Applications and Benefits in a Case Study" class="work-thumbnail" src="https://attachments.academia-assets.com/81696792/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000103/AI_Based_Software_Defect_Predictors_Applications_and_Benefits_in_a_Case_Study">AI-Based Software Defect Predictors: Applications and Benefits in a Case Study</a></div><div class="wp-workCard_item"><span>AI Magazine</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Software defect prediction aims to reduce software testing efforts by guiding testers through the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bb821be64a195fb4f258c67c6b7299e1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696792,"asset_id":73000103,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696792/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000103"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000103"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000103; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000103]").text(description); $(".js-view-count[data-work-id=73000103]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000103; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000103']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000103, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bb821be64a195fb4f258c67c6b7299e1" } } $('.js-work-strip[data-work-id=73000103]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000103,"title":"AI-Based Software Defect Predictors: Applications and Benefits in a Case Study","translated_title":"","metadata":{"abstract":"Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. 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Furthermore, we compared the performance of our model with that of another testing strategy applied in...","publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","publication_name":"AI Magazine"},"translated_abstract":"Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in...","internal_url":"https://www.academia.edu/73000103/AI_Based_Software_Defect_Predictors_Applications_and_Benefits_in_a_Case_Study","translated_internal_url":"","created_at":"2022-03-04T00:19:21.786-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696792,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696792/thumbnails/1.jpg","file_name":"2216.pdf","download_url":"https://www.academia.edu/attachments/81696792/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"AI_Based_Software_Defect_Predictors_Appl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696792/2216-libre.pdf?1646469782=\u0026response-content-disposition=attachment%3B+filename%3DAI_Based_Software_Defect_Predictors_Appl.pdf\u0026Expires=1732998484\u0026Signature=B8ZDHyYUeOucRFkePnnB7NXjpHIksxHrhN4rC~HLGweuIsbwxkWvR~9XlOex9x2VQoQXaWMDW2LGCEsO84B2Lsp3ZC~zyNuDyoEfsysQ0DF4HKHckvq6PQtYZeB7twFhCLHKzoY8hd3ZDKA2~V8s4I7PibRQhiWOyikFJE5CwSonvjNqDYSCtRM4vUTskIaFM28W7tIdQv1MqAEgHxVdlYaGI175AUID9ep0GgN9z588x2vVHp9E-pt-g45sRToUJEmmobJh~J6bTZDIZWClxAjXkjfh9-lFxhjkuzgL7MIdH84X7I1~LFLXPYN9uo14S7ce05E5y1R7ad9WYJmWYQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"AI_Based_Software_Defect_Predictors_Applications_and_Benefits_in_a_Case_Study","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696792,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696792/thumbnails/1.jpg","file_name":"2216.pdf","download_url":"https://www.academia.edu/attachments/81696792/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"AI_Based_Software_Defect_Predictors_Appl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696792/2216-libre.pdf?1646469782=\u0026response-content-disposition=attachment%3B+filename%3DAI_Based_Software_Defect_Predictors_Appl.pdf\u0026Expires=1732998484\u0026Signature=B8ZDHyYUeOucRFkePnnB7NXjpHIksxHrhN4rC~HLGweuIsbwxkWvR~9XlOex9x2VQoQXaWMDW2LGCEsO84B2Lsp3ZC~zyNuDyoEfsysQ0DF4HKHckvq6PQtYZeB7twFhCLHKzoY8hd3ZDKA2~V8s4I7PibRQhiWOyikFJE5CwSonvjNqDYSCtRM4vUTskIaFM28W7tIdQv1MqAEgHxVdlYaGI175AUID9ep0GgN9z588x2vVHp9E-pt-g45sRToUJEmmobJh~J6bTZDIZWClxAjXkjfh9-lFxhjkuzgL7MIdH84X7I1~LFLXPYN9uo14S7ce05E5y1R7ad9WYJmWYQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":81696791,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696791/thumbnails/1.jpg","file_name":"2216.pdf","download_url":"https://www.academia.edu/attachments/81696791/download_file","bulk_download_file_name":"AI_Based_Software_Defect_Predictors_Appl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696791/2216-libre.pdf?1646469782=\u0026response-content-disposition=attachment%3B+filename%3DAI_Based_Software_Defect_Predictors_Appl.pdf\u0026Expires=1732998484\u0026Signature=JNucoEltLDMyfYAb3ucKkS0CeaTkKsL4jgqrZNncLAc2yrNzmEFRD5kfw6zQb~WPD70O8XABE7G3M1QgkCzIrjwldop8AK~EpOYM2Av2zwWdLa-io3oAaQbPHz95Uvqb55Ye~II2BfDU~x1qDTVkAHDBgIrR4oVAJBBco6yvQ52~6pKzJedvudn670yCEQmFhY6Xc12SaKq8M7~AOjSwwu45E1Jwz5VdghdV6htncv4d8BcUAO2RLEioAWCBmHAveE3QU9RAM0uEK6Zw6ARr-V-kgGFOBbgOR3DRNaqdtChd~I~wAcaJg82SsKnE~qjSVbEAGFlMvkh74bw7gKzBOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2277,"name":"Project Management","url":"https://www.academia.edu/Documents/in/Project_Management"},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling"},{"id":8129,"name":"Software Development","url":"https://www.academia.edu/Documents/in/Software_Development"},{"id":21395,"name":"Telecommunication","url":"https://www.academia.edu/Documents/in/Telecommunication"},{"id":28532,"name":"Software Reliability","url":"https://www.academia.edu/Documents/in/Software_Reliability"},{"id":68380,"name":"Software Metrics","url":"https://www.academia.edu/Documents/in/Software_Metrics"},{"id":436756,"name":"Defect","url":"https://www.academia.edu/Documents/in/Defect"}],"urls":[{"id":18227781,"url":"https://aaai.org/ojs/index.php/aimagazine/article/viewFile/2348/2216"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000102"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000102/Practice_does_not_always_make_perfect_need_for_selection_curricula_in_modern_surgical_training"><img alt="Research paper thumbnail of Practice does not always make perfect: need for selection curricula in modern surgical training" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000102/Practice_does_not_always_make_perfect_need_for_selection_curricula_in_modern_surgical_training">Practice does not always make perfect: need for selection curricula in modern surgical training</a></div><div class="wp-workCard_item"><span>Surgical Endoscopy</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is hypothesized that not all surgical trainees are able to reach technical competence despite ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is hypothesized that not all surgical trainees are able to reach technical competence despite ongoing practice. The objectives of the study were to assess a trainees&amp;amp;amp;amp;amp;#39; ability to reach technical competence by assessing learning patterns of the acquisition of surgical skills. Furthermore, it aims to determine whether individuals&amp;amp;amp;amp;amp;#39; learning patterns were consistent across a range of open and laparoscopic tasks of variable difficulty. Sixty-five preclinical medical students participated in a training curriculum with standardized feedback over forty repetitions of the following laparoscopic and open technical tasks: peg transfer (PT), circle cutting (CC), intracorporeal knot tie (IKT), one-handed tie, and simulated laparotomy closure. Data mining techniques were used to analyze the prospectively collected data and stratify the students into four learning clusters. Performance was compared between groups, and learning curve characteristics unique to trainees who have difficulty reaching technical competence were quantified. Top performers (22-35%) and high performers (32-42%) reached proficiency in all tasks. Moderate performers (25-37%) reached proficiency for all open tasks but not all laparoscopic tasks. Low performers (8-15%) failed to reach proficiency in four of five tasks including all laparoscopic tasks (PT 7.8%; CC 9.4%; IKT 15.6%). Participants in lower performance clusters demonstrated sustained performance disadvantage across tasks, with widely variable learning curves and no evidence of progression towards a plateau phase. Most students reached proficiency across a range of surgical tasks, but low-performing trainees failed to reach competence in laparoscopic tasks. With increasing use of laparoscopy in surgical practice, screening potential candidates to identify the lowest performers may be beneficial.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000102"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000102"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000102; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000102]").text(description); $(".js-view-count[data-work-id=73000102]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000102; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000102']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000102, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000102]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000102,"title":"Practice does not always make perfect: need for selection curricula in modern surgical training","translated_title":"","metadata":{"abstract":"It is hypothesized that not all surgical trainees are able to reach technical competence despite ongoing practice. The objectives of the study were to assess a trainees\u0026amp;amp;amp;amp;amp;#39; ability to reach technical competence by assessing learning patterns of the acquisition of surgical skills. Furthermore, it aims to determine whether individuals\u0026amp;amp;amp;amp;amp;#39; learning patterns were consistent across a range of open and laparoscopic tasks of variable difficulty. Sixty-five preclinical medical students participated in a training curriculum with standardized feedback over forty repetitions of the following laparoscopic and open technical tasks: peg transfer (PT), circle cutting (CC), intracorporeal knot tie (IKT), one-handed tie, and simulated laparotomy closure. Data mining techniques were used to analyze the prospectively collected data and stratify the students into four learning clusters. Performance was compared between groups, and learning curve characteristics unique to trainees who have difficulty reaching technical competence were quantified. Top performers (22-35%) and high performers (32-42%) reached proficiency in all tasks. Moderate performers (25-37%) reached proficiency for all open tasks but not all laparoscopic tasks. Low performers (8-15%) failed to reach proficiency in four of five tasks including all laparoscopic tasks (PT 7.8%; CC 9.4%; IKT 15.6%). Participants in lower performance clusters demonstrated sustained performance disadvantage across tasks, with widely variable learning curves and no evidence of progression towards a plateau phase. Most students reached proficiency across a range of surgical tasks, but low-performing trainees failed to reach competence in laparoscopic tasks. With increasing use of laparoscopy in surgical practice, screening potential candidates to identify the lowest performers may be beneficial.","publisher":"Springer Nature","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Surgical Endoscopy"},"translated_abstract":"It is hypothesized that not all surgical trainees are able to reach technical competence despite ongoing practice. The objectives of the study were to assess a trainees\u0026amp;amp;amp;amp;amp;#39; ability to reach technical competence by assessing learning patterns of the acquisition of surgical skills. Furthermore, it aims to determine whether individuals\u0026amp;amp;amp;amp;amp;#39; learning patterns were consistent across a range of open and laparoscopic tasks of variable difficulty. Sixty-five preclinical medical students participated in a training curriculum with standardized feedback over forty repetitions of the following laparoscopic and open technical tasks: peg transfer (PT), circle cutting (CC), intracorporeal knot tie (IKT), one-handed tie, and simulated laparotomy closure. Data mining techniques were used to analyze the prospectively collected data and stratify the students into four learning clusters. Performance was compared between groups, and learning curve characteristics unique to trainees who have difficulty reaching technical competence were quantified. Top performers (22-35%) and high performers (32-42%) reached proficiency in all tasks. Moderate performers (25-37%) reached proficiency for all open tasks but not all laparoscopic tasks. Low performers (8-15%) failed to reach proficiency in four of five tasks including all laparoscopic tasks (PT 7.8%; CC 9.4%; IKT 15.6%). Participants in lower performance clusters demonstrated sustained performance disadvantage across tasks, with widely variable learning curves and no evidence of progression towards a plateau phase. Most students reached proficiency across a range of surgical tasks, but low-performing trainees failed to reach competence in laparoscopic tasks. With increasing use of laparoscopy in surgical practice, screening potential candidates to identify the lowest performers may be beneficial.","internal_url":"https://www.academia.edu/73000102/Practice_does_not_always_make_perfect_need_for_selection_curricula_in_modern_surgical_training","translated_internal_url":"","created_at":"2022-03-04T00:19:21.608-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Practice_does_not_always_make_perfect_need_for_selection_curricula_in_modern_surgical_training","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse 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Estimation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125713181/Exploiting_the_Essential_Assumptions_of_Analogy_Based_Effort_Estimation">Exploiting the Essential Assumptions of Analogy-Based Effort Estimation</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Software Engineering</span><span>, 2012</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d58475e11e17ac5c3750cfb00252fbe6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119706466,"asset_id":125713181,"asset_type":"Work","button_location":"profile"}" 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window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125713181]").text(description); $(".js-view-count[data-work-id=125713181]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125713181; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125713181']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 125713181, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d58475e11e17ac5c3750cfb00252fbe6" } } $('.js-work-strip[data-work-id=125713181]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125713181,"title":"Exploiting the Essential Assumptions of Analogy-Based Effort Estimation","translated_title":"","metadata":{"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"IEEE Transactions on Software 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Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":806332,"name":"Estimator","url":"https://www.academia.edu/Documents/in/Estimator"},{"id":960570,"name":"K NN","url":"https://www.academia.edu/Documents/in/K_NN"},{"id":1555351,"name":"Euclidean Distance","url":"https://www.academia.edu/Documents/in/Euclidean_Distance"},{"id":1838445,"name":"Nearest Neighbor Method","url":"https://www.academia.edu/Documents/in/Nearest_Neighbor_Method"},{"id":2679107,"name":"Effort estimation","url":"https://www.academia.edu/Documents/in/Effort_estimation"}],"urls":[{"id":45714209,"url":"http://xplorestaging.ieee.org/ielx5/32/6173074/05728833.pdf?arnumber=5728833"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="104412772"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104412772/A_systematic_literature_review_on_the_applications_of_Bayesian_networks_to_predict_software_quality"><img alt="Research paper thumbnail of A systematic literature review on the applications of Bayesian networks to predict software quality" class="work-thumbnail" src="https://attachments.academia-assets.com/104150066/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104412772/A_systematic_literature_review_on_the_applications_of_Bayesian_networks_to_predict_software_quality">A systematic literature review on the applications of Bayesian networks to predict software quality</a></div><div class="wp-workCard_item"><span>Software Quality Journal</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5fdc370d03713bece2b09734f5b40258" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104150066,"asset_id":104412772,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104150066/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104412772"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span 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WowProfile.WorkStripView({ el: this, workJSON: {"id":104412772,"title":"A systematic literature review on the applications of Bayesian networks to predict software quality","translated_title":"","metadata":{"publisher":"Springer Science and Business Media LLC","grobid_abstract":"Bayesian networks (BN) have been used for decision making in software engineering for many years. In other fields such as bioinformatics, BNs are rigorously evaluated in terms of the techniques that are used to build the network structure and to learn the parameters. We extend our prior mapping study to investigate the extent to which contextual and methodological details regarding BN construction are reported in the studies. We conduct a systematic literature review on the applications of BNs to predict software quality. We focus on more detailed questions regarding (1) dataset characteristics, (2) techniques used for parameter learning, (3) techniques used for structure learning, (4) use of tools, and (5) model validation techniques. Results on ten primary studies show that BNs are mostly built based on expert knowledge, i.e. structure and prior distributions are defined by experts, whereas authors benefit from BN tools and quantitative data to validate their models. In most of the papers, authors do not clearly explain their justification for choosing a specific technique, and they do not compare their proposed BNs with other machine learning approaches. There is also a lack of consensus on the performance measures to validate the proposed BNs. Compared to other domains, the use of BNs is still very limited and current publications do not report enough details to replicate the studies. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98025366"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98025366/Predictive_analytics_in_healthcare_epileptic_seizure_recognition"><img alt="Research paper thumbnail of Predictive analytics in healthcare epileptic seizure recognition" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98025366/Predictive_analytics_in_healthcare_epileptic_seizure_recognition">Predictive analytics in healthcare epileptic seizure recognition</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Introduction Clinical applications of electroencephalography (EEG) span a very broad range of dia...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Introduction Clinical applications of electroencephalography (EEG) span a very broad range of diagnostic conditions. Epileptic seizure is the fourth most common neurological disorder in that. Related Work There has been considerable progress in clinical understanding of epilepsy, however many aspects of seizure prevention are still a mystery. Predictive modeling of EEG can provide significant value addition to substantiate the diagnosis of epilepsy. Methodology Machine learning algorithms are applied to predict the probability of epileptic seizure using an open source multi-class dataset. Results and Discussion Comparing the F-score from different classifiers, it is found that XGBoost gives the best performance in binary classification and Random Forest provides the best performance in multinomial classification. Conclusion Our results show that it is possible to predict epileptic seizure with significant accuracy from non-epileptic parameters using a suitable machine learning algor...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98025366"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98025366"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98025366; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98025366]").text(description); $(".js-view-count[data-work-id=98025366]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98025366; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98025366']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 98025366, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=98025366]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":98025366,"title":"Predictive analytics in healthcare epileptic seizure recognition","translated_title":"","metadata":{"abstract":"Introduction Clinical applications of electroencephalography (EEG) span a very broad range of diagnostic conditions. Epileptic seizure is the fourth most common neurological disorder in that. Related Work There has been considerable progress in clinical understanding of epilepsy, however many aspects of seizure prevention are still a mystery. Predictive modeling of EEG can provide significant value addition to substantiate the diagnosis of epilepsy. Methodology Machine learning algorithms are applied to predict the probability of epileptic seizure using an open source multi-class dataset. Results and Discussion Comparing the F-score from different classifiers, it is found that XGBoost gives the best performance in binary classification and Random Forest provides the best performance in multinomial classification. Conclusion Our results show that it is possible to predict epileptic seizure with significant accuracy from non-epileptic parameters using a suitable machine learning algor...","publisher":"CASCON","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"Introduction Clinical applications of electroencephalography (EEG) span a very broad range of diagnostic conditions. Epileptic seizure is the fourth most common neurological disorder in that. Related Work There has been considerable progress in clinical understanding of epilepsy, however many aspects of seizure prevention are still a mystery. Predictive modeling of EEG can provide significant value addition to substantiate the diagnosis of epilepsy. Methodology Machine learning algorithms are applied to predict the probability of epileptic seizure using an open source multi-class dataset. Results and Discussion Comparing the F-score from different classifiers, it is found that XGBoost gives the best performance in binary classification and Random Forest provides the best performance in multinomial classification. Conclusion Our results show that it is possible to predict epileptic seizure with significant accuracy from non-epileptic parameters using a suitable machine learning algor...","internal_url":"https://www.academia.edu/98025366/Predictive_analytics_in_healthcare_epileptic_seizure_recognition","translated_internal_url":"","created_at":"2023-03-06T05:16:18.218-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Predictive_analytics_in_healthcare_epileptic_seizure_recognition","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":7648,"name":"Epilepsy","url":"https://www.academia.edu/Documents/in/Epilepsy"},{"id":10904,"name":"Electroencephalography","url":"https://www.academia.edu/Documents/in/Electroencephalography"},{"id":70995,"name":"Random Forest","url":"https://www.academia.edu/Documents/in/Random_Forest"},{"id":154214,"name":"Binary Classification","url":"https://www.academia.edu/Documents/in/Binary_Classification"}],"urls":[{"id":29535716,"url":"https://dl.acm.org/citation.cfm?id=3291327"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98002162"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98002162/An_Improvement_to_Test_Case_Failure_Prediction_in_the_Context_of_Test_Case_Prioritization"><img alt="Research paper thumbnail of An Improvement to Test Case Failure Prediction in the Context of Test Case Prioritization" class="work-thumbnail" src="https://attachments.academia-assets.com/99472653/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98002162/An_Improvement_to_Test_Case_Failure_Prediction_in_the_Context_of_Test_Case_Prioritization">An Improvement to Test Case Failure Prediction in the Context of Test Case Prioritization</a></div><div class="wp-workCard_item"><span>Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f205e7ee0c64e8edd7acd8c50d4959f6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":99472653,"asset_id":98002162,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/99472653/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98002162"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98002162"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98002162; 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Background: The process of prioritizing test cases aims to come up with a ranked test suite where test cases meeting certain criteria are prioritized. One criterion may be the ability of test cases to nd faults that can be predicted a priori. Ranking test cases and executing the top-ranked test cases is particularly benecial when projects have tight schedules and budgets. Method: We performed the comparison by rst rebuilding the predictive models using the features from the original study and then we extended the original work to improve the predictive models using new features by combining with the existing ones. Results: The results of our study, using a dataset of ve open-source systems, conrm that the ndings from the original study hold and that our predictive models with new features outperform the original models in predicting and prioritizing the failing test cases. 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Bu kavram de 搂i鲁iklik ba 搂la鲁m olarak tanmlanmaktadr. Bu 莽al鲁mada end眉striyel bir yazlm geli鲁tirme ortamnda de 搂i鲁iklik ba 搂la鲁m ile yazlm hatalarnn ili鲁kisi ara鲁trlm鲁tr.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Ulusal Yaz谋l谋m M眉hendisli臒i Sempozyumu","grobid_abstract_attachment_id":81696897},"translated_abstract":null,"internal_url":"https://www.academia.edu/73000119/De%C4%9Fi%C5%9Fiklik_Ba%C4%9Fla%C5%9F%C4%B1m%C4%B1_ve_Yaz%C4%B1l%C4%B1m_Hatalar%C4%B1_%C4%B0li%C5%9Fkisinin_%C4%B0ncelenmesi","translated_internal_url":"","created_at":"2022-03-04T00:19:26.257-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696897,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696897/thumbnails/1.jpg","file_name":"40_Bildiri.pdf","download_url":"https://www.academia.edu/attachments/81696897/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Degisiklik_Baglasimi_ve_Yazilim_Hatalari.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696897/40_Bildiri-libre.pdf?1646469772=\u0026response-content-disposition=attachment%3B+filename%3DDegisiklik_Baglasimi_ve_Yazilim_Hatalari.pdf\u0026Expires=1732998483\u0026Signature=fdYeTvoEuGAm6ACf28gu-auKsHEGIpCWY4ziWyhX9kCxJpU01IovfxcXBzTBaXalFCdX8dg2iA-UgYSHp7laQdt0s4zA-NnzX4jipKOqMotx5uTn9l-6F6IszZF~fCbmWTR6i60p~anRZqXixo7TIu7S0siGxESqE2SRRRBkrP2znwZEMriXA3Mu2P2d2a5og1bCjg-yKhjiIJJyFg3ZgUPvbc2WDLSUqvlFm3TjrytqquSWzMgydkpIhhPAQR-KvelQH~BtKFbLlLxBieJPznOFTHFHgBQ3i0NvzMOBDQOK03tckHEZSpVjrhwmybr4iaSZQv81H~CyHUj9QHHeqw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"De臒i艧iklik_Ba臒la艧谋m谋_ve_Yaz谋l谋m_Hatalar谋_陌li艧kisinin_陌ncelenmesi","translated_slug":"","page_count":12,"language":"tr","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696897,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696897/thumbnails/1.jpg","file_name":"40_Bildiri.pdf","download_url":"https://www.academia.edu/attachments/81696897/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Degisiklik_Baglasimi_ve_Yazilim_Hatalari.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696897/40_Bildiri-libre.pdf?1646469772=\u0026response-content-disposition=attachment%3B+filename%3DDegisiklik_Baglasimi_ve_Yazilim_Hatalari.pdf\u0026Expires=1732998483\u0026Signature=fdYeTvoEuGAm6ACf28gu-auKsHEGIpCWY4ziWyhX9kCxJpU01IovfxcXBzTBaXalFCdX8dg2iA-UgYSHp7laQdt0s4zA-NnzX4jipKOqMotx5uTn9l-6F6IszZF~fCbmWTR6i60p~anRZqXixo7TIu7S0siGxESqE2SRRRBkrP2znwZEMriXA3Mu2P2d2a5og1bCjg-yKhjiIJJyFg3ZgUPvbc2WDLSUqvlFm3TjrytqquSWzMgydkpIhhPAQR-KvelQH~BtKFbLlLxBieJPznOFTHFHgBQ3i0NvzMOBDQOK03tckHEZSpVjrhwmybr4iaSZQv81H~CyHUj9QHHeqw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000118"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000118/Neural_Network_Based_Spectrum_Prediction_in_Land_Mobile_Radio_Bands_for_IoT_deployments"><img alt="Research paper thumbnail of Neural Network Based Spectrum Prediction in Land Mobile Radio Bands for IoT deployments" class="work-thumbnail" src="https://attachments.academia-assets.com/81696896/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000118/Neural_Network_Based_Spectrum_Prediction_in_Land_Mobile_Radio_Bands_for_IoT_deployments">Neural Network Based Spectrum Prediction in Land Mobile Radio Bands for IoT deployments</a></div><div class="wp-workCard_item"><span>IFIP/IEEE Symposium on Integrated Network Management</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1113ece7eb6b77051c7b1a9fcbf781f9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696896,"asset_id":73000118,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696896/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000118"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000118"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000118; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000118]").text(description); $(".js-view-count[data-work-id=73000118]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000118; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000118']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000118, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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This could lead to dynamic spectrum allocation methods to address potential spectrum shortages facing Internet of Things (IoT) deployments. Background: ANNs are a popular choice for spectrum prediction. Traditionally, ARIMA models have been at the forefront of forecasting and prediction but ANNs that learn from time series have demonstrated good performance using both simulated datasets and real-life data collected in the cellular bands. Methodology: We use three prediction models, a baseline which simply delays the time series, a seasonal ARIMA model and a TDNN. We test their performance on an hourly dataset in LMR bands collected in Ottawa, Canada between the dates of October 2016 and April 2017. Results: We demonstrate that TDNN yields improvements over seasonal ARIMA models in predicting short time horizons. Conclusions: The TDNN based prediction models that are designed to work with time series data provide a better alternative for accurately predicting spectrum occupancy in bands that exhibit similar characteristics to LMR channels, especially as the forecast horizon gets longer.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"IFIP/IEEE Symposium on Integrated Network Management","grobid_abstract_attachment_id":81696896},"translated_abstract":null,"internal_url":"https://www.academia.edu/73000118/Neural_Network_Based_Spectrum_Prediction_in_Land_Mobile_Radio_Bands_for_IoT_deployments","translated_internal_url":"","created_at":"2022-03-04T00:19:25.780-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696896,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696896/thumbnails/1.jpg","file_name":"2019AnNet.pdf","download_url":"https://www.academia.edu/attachments/81696896/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Neural_Network_Based_Spectrum_Prediction.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696896/2019AnNet-libre.pdf?1646469769=\u0026response-content-disposition=attachment%3B+filename%3DNeural_Network_Based_Spectrum_Prediction.pdf\u0026Expires=1732998483\u0026Signature=SRmsEp5kwHqvD67GIT6jiwtzTaeuyReR-I8wXqN05Ht79RMlT9CiRfkjGleijJMFBRx5MDNLwhsQq5XgHfxR2BSz-37PPZ-8UPqpGPbgnn1O1q3N21uz~IyO~xZFRUcR0eXQ8Q0WicsfhVwBKCPzOCSvCVHeB8fmjl1twnlw1I6NFtR3eYuhiaPFZcIdQKRmJ-rrLUY95QKRr1Ow0hQej9DXPyzhIIfmienOoi5Am1vEKAlkhrNeIH4rKwdrAZGQkL5hvQcBosdex8PTR1uq0MoFEKnv0IkS1m5Tv7Jo0RPMxt71eKgc07ivFprWJIUrAQa0TtNOd7NgZaCQfqpS~A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Neural_Network_Based_Spectrum_Prediction_in_Land_Mobile_Radio_Bands_for_IoT_deployments","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696896,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696896/thumbnails/1.jpg","file_name":"2019AnNet.pdf","download_url":"https://www.academia.edu/attachments/81696896/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Neural_Network_Based_Spectrum_Prediction.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696896/2019AnNet-libre.pdf?1646469769=\u0026response-content-disposition=attachment%3B+filename%3DNeural_Network_Based_Spectrum_Prediction.pdf\u0026Expires=1732998483\u0026Signature=SRmsEp5kwHqvD67GIT6jiwtzTaeuyReR-I8wXqN05Ht79RMlT9CiRfkjGleijJMFBRx5MDNLwhsQq5XgHfxR2BSz-37PPZ-8UPqpGPbgnn1O1q3N21uz~IyO~xZFRUcR0eXQ8Q0WicsfhVwBKCPzOCSvCVHeB8fmjl1twnlw1I6NFtR3eYuhiaPFZcIdQKRmJ-rrLUY95QKRr1Ow0hQej9DXPyzhIIfmienOoi5Am1vEKAlkhrNeIH4rKwdrAZGQkL5hvQcBosdex8PTR1uq0MoFEKnv0IkS1m5Tv7Jo0RPMxt71eKgc07ivFprWJIUrAQa0TtNOd7NgZaCQfqpS~A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000117"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000117/The_Impact_of_Confirmation_Bias_on_the_Release_based_Defect_Prediction_of_Developer_Groups"><img alt="Research paper thumbnail of The Impact of Confirmation Bias on the Release-based Defect Prediction of Developer Groups" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000117/The_Impact_of_Confirmation_Bias_on_the_Release_based_Defect_Prediction_of_Developer_Groups">The Impact of Confirmation Bias on the Release-based Defect Prediction of Developer Groups</a></div><div class="wp-workCard_item"><span>International Conference on Software Engineering and Knowledge Engineering</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT During software development life cycle (SDLC), source codes are created and updated by g...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT During software development life cycle (SDLC), source codes are created and updated by groups of one or more developers. Information about the defect rates introduced by developer groups for the current release of a software product might guide project managers to form developer groups in order to manage defect rates for the next releases. In this research, we use partial least squares regression (PLSR) and principal component regression (PCR) to model the relation between defect rates and a specific cognitive aspect of developers, namely confirmation bias. In order to empirically estimate the performance of our model, we use datasets from three industrial software projects.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000117"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000117"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000117; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000117]").text(description); $(".js-view-count[data-work-id=73000117]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000117; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000117']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000117, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000117]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000117,"title":"The Impact of Confirmation Bias on the Release-based Defect Prediction of Developer Groups","translated_title":"","metadata":{"abstract":"ABSTRACT During software development life cycle (SDLC), source codes are created and updated by groups of one or more developers. Information about the defect rates introduced by developer groups for the current release of a software product might guide project managers to form developer groups in order to manage defect rates for the next releases. In this research, we use partial least squares regression (PLSR) and principal component regression (PCR) to model the relation between defect rates and a specific cognitive aspect of developers, namely confirmation bias. In order to empirically estimate the performance of our model, we use datasets from three industrial software projects.","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"International Conference on Software Engineering and Knowledge Engineering"},"translated_abstract":"ABSTRACT During software development life cycle (SDLC), source codes are created and updated by groups of one or more developers. Information about the defect rates introduced by developer groups for the current release of a software product might guide project managers to form developer groups in order to manage defect rates for the next releases. In this research, we use partial least squares regression (PLSR) and principal component regression (PCR) to model the relation between defect rates and a specific cognitive aspect of developers, namely confirmation bias. In order to empirically estimate the performance of our model, we use datasets from three industrial software projects.","internal_url":"https://www.academia.edu/73000117/The_Impact_of_Confirmation_Bias_on_the_Release_based_Defect_Prediction_of_Developer_Groups","translated_internal_url":"","created_at":"2022-03-04T00:19:25.463-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Impact_of_Confirmation_Bias_on_the_Release_based_Defect_Prediction_of_Developer_Groups","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000116"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000116/Predicting_Implantation_Outcome_from_Imbalanced_IVF_Dataset"><img alt="Research paper thumbnail of Predicting Implantation Outcome from Imbalanced IVF Dataset" class="work-thumbnail" src="https://attachments.academia-assets.com/83892387/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000116/Predicting_Implantation_Outcome_from_Imbalanced_IVF_Dataset">Predicting Implantation Outcome from Imbalanced IVF Dataset</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract-Predicting implantation outcomes of invitro fertilization (IVF) embryos is critical for ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract-Predicting implantation outcomes of invitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied Naive Bayes classifier to an original IVF dataset in order to discriminate embryos according to implantation potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of imbalance, we examined the effects of over sampling the minority class, under sampling the majority class and adjustment of the decision threshold on the classification performance. We have used features of Receiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve same prediction performance with less data as well as 736 embryo samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="553874e0991e1e1b57984ecc48c453f4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83892387,"asset_id":73000116,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83892387/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000116"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000116"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000116; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000116]").text(description); $(".js-view-count[data-work-id=73000116]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000116; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000116']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000116, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "553874e0991e1e1b57984ecc48c453f4" } } $('.js-work-strip[data-work-id=73000116]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000116,"title":"Predicting Implantation Outcome from Imbalanced IVF Dataset","translated_title":"","metadata":{"abstract":"Abstract-Predicting implantation outcomes of invitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied Naive Bayes classifier to an original IVF dataset in order to discriminate embryos according to implantation potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of imbalance, we examined the effects of over sampling the minority class, under sampling the majority class and adjustment of the decision threshold on the classification performance. We have used features of Receiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve same prediction performance with less data as well as 736 embryo samples.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"Abstract-Predicting implantation outcomes of invitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied Naive Bayes classifier to an original IVF dataset in order to discriminate embryos according to implantation potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of imbalance, we examined the effects of over sampling the minority class, under sampling the majority class and adjustment of the decision threshold on the classification performance. We have used features of Receiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve same prediction performance with less data as well as 736 embryo samples.","internal_url":"https://www.academia.edu/73000116/Predicting_Implantation_Outcome_from_Imbalanced_IVF_Dataset","translated_internal_url":"","created_at":"2022-03-04T00:19:25.200-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":83892387,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/83892387/thumbnails/1.jpg","file_name":"Predicting_Implantation_Outcome_from_Imb20220411-30673-lr27oq.pdf","download_url":"https://www.academia.edu/attachments/83892387/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predicting_Implantation_Outcome_from_Imb.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/83892387/Predicting_Implantation_Outcome_from_Imb20220411-30673-lr27oq.pdf?1649727377=\u0026response-content-disposition=attachment%3B+filename%3DPredicting_Implantation_Outcome_from_Imb.pdf\u0026Expires=1732998483\u0026Signature=BsyVC1HK0wo0pD~ehigft6SWegGvBBdtVKaa9zm5bvo83v37psktlKDhW3ZRbyJWD7X6lbYdQGh5vRw4j1uY~EkD89ZVm-Ag5ctiLueqaeqFeetKqM05rHWj~k02Ea5y0dfo3nreHcqzkDGPsRpzJVEe-f38hZqJ1cfjNOcIUDNe-u~Ndcob326TQRM4uO1O-tPzLHbzQO9XpTKFyWWYf7iQQBgBjDO9-YW3CD6M3NdDPE2uu5Mn4oi4TpQUGCXD6tsni250Tmkyrwy0S0XgbsBrid3fUknGq7ByBF0VM-EFZl6gHmMdUIX~5-xNqkWLTEFbbsL~vOXKSkOUTLbK4A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Predicting_Implantation_Outcome_from_Imbalanced_IVF_Dataset","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":83892387,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/83892387/thumbnails/1.jpg","file_name":"Predicting_Implantation_Outcome_from_Imb20220411-30673-lr27oq.pdf","download_url":"https://www.academia.edu/attachments/83892387/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predicting_Implantation_Outcome_from_Imb.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/83892387/Predicting_Implantation_Outcome_from_Imb20220411-30673-lr27oq.pdf?1649727377=\u0026response-content-disposition=attachment%3B+filename%3DPredicting_Implantation_Outcome_from_Imb.pdf\u0026Expires=1732998483\u0026Signature=BsyVC1HK0wo0pD~ehigft6SWegGvBBdtVKaa9zm5bvo83v37psktlKDhW3ZRbyJWD7X6lbYdQGh5vRw4j1uY~EkD89ZVm-Ag5ctiLueqaeqFeetKqM05rHWj~k02Ea5y0dfo3nreHcqzkDGPsRpzJVEe-f38hZqJ1cfjNOcIUDNe-u~Ndcob326TQRM4uO1O-tPzLHbzQO9XpTKFyWWYf7iQQBgBjDO9-YW3CD6M3NdDPE2uu5Mn4oi4TpQUGCXD6tsni250Tmkyrwy0S0XgbsBrid3fUknGq7ByBF0VM-EFZl6gHmMdUIX~5-xNqkWLTEFbbsL~vOXKSkOUTLbK4A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":18227790,"url":"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1068.3142\u0026rep=rep1\u0026type=pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000115"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000115/On_Empirically_Examining_The_Effectiveness_Of_Deep_Learning_Based_Bug_Localization_Models"><img alt="Research paper thumbnail of On Empirically Examining The Effectiveness Of Deep Learning-Based Bug Localization Models" class="work-thumbnail" src="https://attachments.academia-assets.com/81696894/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000115/On_Empirically_Examining_The_Effectiveness_Of_Deep_Learning_Based_Bug_Localization_Models">On Empirically Examining The Effectiveness Of Deep Learning-Based Bug Localization Models</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Software Bug Localization involves a significant amount of time and effort on the part of the sof...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Software Bug Localization involves a significant amount of time and effort on the part of the software developer. Many state-of-the-art bug localization models have been proposed in the past, to help developers localize bugs easily. However, none of these models meet the adoption thresholds of the software practitioner. Recently some deep learning-based models have been proposed, that have been shown to perform better than the state-of-the-art models. With this motivation, we experiment on Convolution Neural Networks (CNNs) to examine their effectiveness in localizing bugs. We also train a SimpleLogistic model as a baseline model for our experiments. We train both our models on five open source Java projects and compare their performance across the projects. Our experiments show that the CNN models perform better than the SimpleLogistic models in most of the cases, but do not meet the adoption criteria set by the practitioners.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="af4a4ad55aaa410820fb842c2d7e911d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696894,"asset_id":73000115,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696894/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000115"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000115"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000115; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000115]").text(description); $(".js-view-count[data-work-id=73000115]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000115; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000115']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000115, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "af4a4ad55aaa410820fb842c2d7e911d" } } $('.js-work-strip[data-work-id=73000115]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000115,"title":"On Empirically Examining The Effectiveness Of Deep Learning-Based Bug Localization Models","translated_title":"","metadata":{"abstract":"Software Bug Localization involves a significant amount of time and effort on the part of the software developer. Many state-of-the-art bug localization models have been proposed in the past, to help developers localize bugs easily. However, none of these models meet the adoption thresholds of the software practitioner. Recently some deep learning-based models have been proposed, that have been shown to perform better than the state-of-the-art models. With this motivation, we experiment on Convolution Neural Networks (CNNs) to examine their effectiveness in localizing bugs. We also train a SimpleLogistic model as a baseline model for our experiments. We train both our models on five open source Java projects and compare their performance across the projects. Our experiments show that the CNN models perform better than the SimpleLogistic models in most of the cases, but do not meet the adoption criteria set by the practitioners.","publisher":"Ryerson University Library and Archives"},"translated_abstract":"Software Bug Localization involves a significant amount of time and effort on the part of the software developer. Many state-of-the-art bug localization models have been proposed in the past, to help developers localize bugs easily. However, none of these models meet the adoption thresholds of the software practitioner. Recently some deep learning-based models have been proposed, that have been shown to perform better than the state-of-the-art models. With this motivation, we experiment on Convolution Neural Networks (CNNs) to examine their effectiveness in localizing bugs. We also train a SimpleLogistic model as a baseline model for our experiments. We train both our models on five open source Java projects and compare their performance across the projects. Our experiments show that the CNN models perform better than the SimpleLogistic models in most of the cases, but do not meet the adoption criteria set by the practitioners.","internal_url":"https://www.academia.edu/73000115/On_Empirically_Examining_The_Effectiveness_Of_Deep_Learning_Based_Bug_Localization_Models","translated_internal_url":"","created_at":"2022-03-04T00:19:25.007-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696894,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696894/thumbnails/1.jpg","file_name":"Sravya_Sravya.pdf","download_url":"https://www.academia.edu/attachments/81696894/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"On_Empirically_Examining_The_Effectivene.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696894/Sravya_Sravya-libre.pdf?1646469783=\u0026response-content-disposition=attachment%3B+filename%3DOn_Empirically_Examining_The_Effectivene.pdf\u0026Expires=1732998483\u0026Signature=HZ-OM4VHCnA6vX5f6tqszCevxbEAVXIwngriggBKR8FlG6BdG3e73paLtI0pHr2E2Z91DaeI0pHg17ICZEN8gsbjOootcfdQoWmNXPR-jKcJbUAcKmDyzM7gChrtBv1isM7pueNKGV7CCbRetNvQZwGZjs1jm38PHJLrVfAJH7jXpa5v5xKCa-VUEAxXmp9vtx~SXZHcKWc-mt3uVdYX0U6qV-Hl21mtYPgARvKByQCBqFo~Kx0yWi8PcfUiak0CpW2sd3bKy7Vx-0L4Zklk34vnVXGEPuVxPLA1Gb2ewnpHXS3HXIR8edElM1H1vHnuAtqTjnfcRMU8CeDp9KZ-rQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"On_Empirically_Examining_The_Effectiveness_Of_Deep_Learning_Based_Bug_Localization_Models","translated_slug":"","page_count":160,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696894,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696894/thumbnails/1.jpg","file_name":"Sravya_Sravya.pdf","download_url":"https://www.academia.edu/attachments/81696894/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"On_Empirically_Examining_The_Effectivene.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696894/Sravya_Sravya-libre.pdf?1646469783=\u0026response-content-disposition=attachment%3B+filename%3DOn_Empirically_Examining_The_Effectivene.pdf\u0026Expires=1732998483\u0026Signature=HZ-OM4VHCnA6vX5f6tqszCevxbEAVXIwngriggBKR8FlG6BdG3e73paLtI0pHr2E2Z91DaeI0pHg17ICZEN8gsbjOootcfdQoWmNXPR-jKcJbUAcKmDyzM7gChrtBv1isM7pueNKGV7CCbRetNvQZwGZjs1jm38PHJLrVfAJH7jXpa5v5xKCa-VUEAxXmp9vtx~SXZHcKWc-mt3uVdYX0U6qV-Hl21mtYPgARvKByQCBqFo~Kx0yWi8PcfUiak0CpW2sd3bKy7Vx-0L4Zklk34vnVXGEPuVxPLA1Gb2ewnpHXS3HXIR8edElM1H1vHnuAtqTjnfcRMU8CeDp9KZ-rQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000114"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000114/Risk_Parity_Models_for_Portfolio_Optimization_A_Study_of_the_Toronto_Stock_Exchange"><img alt="Research paper thumbnail of Risk Parity Models for Portfolio Optimization: A Study of the Toronto Stock Exchange" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000114/Risk_Parity_Models_for_Portfolio_Optimization_A_Study_of_the_Toronto_Stock_Exchange">Risk Parity Models for Portfolio Optimization: A Study of the Toronto Stock Exchange</a></div><div class="wp-workCard_item"><span>2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It has been more than 60 years since the development of Mean-Variance (MV) framework and inceptio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It has been more than 60 years since the development of Mean-Variance (MV) framework and inception of Modern Portfolio theory. Despite its wide acceptance and applicability, it suffers from few limitations. This paper addresses two issues of MV framework: (i) estimation errors of mean-variance model, and (ii) instability of covariance matrix. Risk parity models, robust statistics and robust optimization minimize the effects of estimation errors of parameters of MV framework. The paper presents two such risk parity models for portfolio optimization, namely, (a) Hierarchical Risk Parity model based on Historical correlation (HRP-HC), and (b) Hierarchical Risk parity model based on Gerber statistics (HRP-GS). The models are tested and analysed using stocks comprising the TSX complete index for a time period of 10 years ranging from 2007 to 2016. Results suggest that the proposed HRP-GS model outperforms HRP-HC model. This is due to the fact that the HRP-GS model integrates the advantages of a risk parity model (i.e. HRP) and robust statistics (i.e. Gerber statistics).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000114"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000114"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000114; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000114]").text(description); $(".js-view-count[data-work-id=73000114]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000114; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000114']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000114, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000114]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000114,"title":"Risk Parity Models for Portfolio Optimization: A Study of the Toronto Stock Exchange","translated_title":"","metadata":{"abstract":"It has been more than 60 years since the development of Mean-Variance (MV) framework and inception of Modern Portfolio theory. Despite its wide acceptance and applicability, it suffers from few limitations. This paper addresses two issues of MV framework: (i) estimation errors of mean-variance model, and (ii) instability of covariance matrix. Risk parity models, robust statistics and robust optimization minimize the effects of estimation errors of parameters of MV framework. The paper presents two such risk parity models for portfolio optimization, namely, (a) Hierarchical Risk Parity model based on Historical correlation (HRP-HC), and (b) Hierarchical Risk parity model based on Gerber statistics (HRP-GS). The models are tested and analysed using stocks comprising the TSX complete index for a time period of 10 years ranging from 2007 to 2016. Results suggest that the proposed HRP-GS model outperforms HRP-HC model. This is due to the fact that the HRP-GS model integrates the advantages of a risk parity model (i.e. HRP) and robust statistics (i.e. Gerber statistics).","publisher":"IEEE","publication_name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)"},"translated_abstract":"It has been more than 60 years since the development of Mean-Variance (MV) framework and inception of Modern Portfolio theory. Despite its wide acceptance and applicability, it suffers from few limitations. This paper addresses two issues of MV framework: (i) estimation errors of mean-variance model, and (ii) instability of covariance matrix. Risk parity models, robust statistics and robust optimization minimize the effects of estimation errors of parameters of MV framework. The paper presents two such risk parity models for portfolio optimization, namely, (a) Hierarchical Risk Parity model based on Historical correlation (HRP-HC), and (b) Hierarchical Risk parity model based on Gerber statistics (HRP-GS). The models are tested and analysed using stocks comprising the TSX complete index for a time period of 10 years ranging from 2007 to 2016. Results suggest that the proposed HRP-GS model outperforms HRP-HC model. This is due to the fact that the HRP-GS model integrates the advantages of a risk parity model (i.e. HRP) and robust statistics (i.e. Gerber statistics).","internal_url":"https://www.academia.edu/73000114/Risk_Parity_Models_for_Portfolio_Optimization_A_Study_of_the_Toronto_Stock_Exchange","translated_internal_url":"","created_at":"2022-03-04T00:19:24.712-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Risk_Parity_Models_for_Portfolio_Optimization_A_Study_of_the_Toronto_Stock_Exchange","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[],"urls":[{"id":18227789,"url":"http://xplorestaging.ieee.org/ielx7/8870906/8876888/08876902.pdf?arnumber=8876902"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000111"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000111/Handling_the_Imbalance_Problem_of_IVF_Implantation_Prediction"><img alt="Research paper thumbnail of Handling the Imbalance Problem of IVF Implantation Prediction" class="work-thumbnail" src="https://attachments.academia-assets.com/81696801/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000111/Handling_the_Imbalance_Problem_of_IVF_Implantation_Prediction">Handling the Imbalance Problem of IVF Implantation Prediction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Predicting implantation outcomes of in- vitro fertilization (IVF) embryos is critical for the suc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Predicting implantation outcomes of in- vitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied the Naive Bayes classifler to an original IVF dataset in order to discriminate embryos according to the implanta- tion potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of im- balance, we examined the efiects of oversampling the minority class, undersampling the majority class and the adjustment of the decision threshold on the clas- siflcation performance. We have used features of Re- ceiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve the same prediction performance with less data as well as 736 embryo samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="add1d825be06dabce328d434299816ea" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696801,"asset_id":73000111,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696801/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000111"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000111"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000111; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000111]").text(description); $(".js-view-count[data-work-id=73000111]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000111; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000111']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000111, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "add1d825be06dabce328d434299816ea" } } $('.js-work-strip[data-work-id=73000111]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000111,"title":"Handling the Imbalance Problem of IVF Implantation Prediction","translated_title":"","metadata":{"abstract":"Predicting implantation outcomes of in- vitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied the Naive Bayes classifler to an original IVF dataset in order to discriminate embryos according to the implanta- tion potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of im- balance, we examined the efiects of oversampling the minority class, undersampling the majority class and the adjustment of the decision threshold on the clas- siflcation performance. We have used features of Re- ceiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve the same prediction performance with less data as well as 736 embryo samples.","publication_date":{"day":null,"month":null,"year":2010,"errors":{}}},"translated_abstract":"Predicting implantation outcomes of in- vitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied the Naive Bayes classifler to an original IVF dataset in order to discriminate embryos according to the implanta- tion potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of im- balance, we examined the efiects of oversampling the minority class, undersampling the majority class and the adjustment of the decision threshold on the clas- siflcation performance. We have used features of Re- ceiver Operating Characteristics (ROC) curves in the evaluation of experiments. Our results revealed that it is possible to obtain optimum True Positive and False Positive Rates simply by adjusting the decision threshold. Under-sampling experiments show that we can achieve the same prediction performance with less data as well as 736 embryo samples.","internal_url":"https://www.academia.edu/73000111/Handling_the_Imbalance_Problem_of_IVF_Implantation_Prediction","translated_internal_url":"","created_at":"2022-03-04T00:19:23.704-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696801,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696801/thumbnails/1.jpg","file_name":"IJCS_37_2_06.pdf","download_url":"https://www.academia.edu/attachments/81696801/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Handling_the_Imbalance_Problem_of_IVF_Im.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696801/IJCS_37_2_06-libre.pdf?1646469777=\u0026response-content-disposition=attachment%3B+filename%3DHandling_the_Imbalance_Problem_of_IVF_Im.pdf\u0026Expires=1732998484\u0026Signature=IXoRnVIz-icxEm1sI5kGDNUUSrpopxQw9vlGAFgyZJ7rgjg5chsIDRwzNxLnOw3PIqdn6UHuWnNZzeRSsX8okG8bXYUh6zdb8KWtWzQbjNV6glovMrTdm2x1iN6qMTtCmOoW~Z6YJMs~8xThrbBSh-m2BY2J4huFvf856WK8QvB3HSMlT8bw~2xz3PxMrewrUOqB7dCcnQF3fYNKqH143e3RbjtGD-oReL0K9rPNQMjWs0yhp6lHhfd-gAbX-CDMhvh~~cYe5VMnuj0YrnTFVZZ5CNtY7WYlGDP1dLpP8NsvrVggPTqU-WmN0fDnGHzGhXwO6TOkP9oEWBJPlI5tRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Handling_the_Imbalance_Problem_of_IVF_Implantation_Prediction","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696801,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696801/thumbnails/1.jpg","file_name":"IJCS_37_2_06.pdf","download_url":"https://www.academia.edu/attachments/81696801/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Handling_the_Imbalance_Problem_of_IVF_Im.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696801/IJCS_37_2_06-libre.pdf?1646469777=\u0026response-content-disposition=attachment%3B+filename%3DHandling_the_Imbalance_Problem_of_IVF_Im.pdf\u0026Expires=1732998484\u0026Signature=IXoRnVIz-icxEm1sI5kGDNUUSrpopxQw9vlGAFgyZJ7rgjg5chsIDRwzNxLnOw3PIqdn6UHuWnNZzeRSsX8okG8bXYUh6zdb8KWtWzQbjNV6glovMrTdm2x1iN6qMTtCmOoW~Z6YJMs~8xThrbBSh-m2BY2J4huFvf856WK8QvB3HSMlT8bw~2xz3PxMrewrUOqB7dCcnQF3fYNKqH143e3RbjtGD-oReL0K9rPNQMjWs0yhp6lHhfd-gAbX-CDMhvh~~cYe5VMnuj0YrnTFVZZ5CNtY7WYlGDP1dLpP8NsvrVggPTqU-WmN0fDnGHzGhXwO6TOkP9oEWBJPlI5tRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"}],"urls":[{"id":18227787,"url":"http://www.iaeng.org/IJCS/issues_v37/issue_2/IJCS_37_2_06.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000110"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000110/Predicting_Commentaries_on_a_Financial_Report_with_Recurrent_Neural_Networks"><img alt="Research paper thumbnail of Predicting Commentaries on a Financial Report with Recurrent Neural Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000110/Predicting_Commentaries_on_a_Financial_Report_with_Recurrent_Neural_Networks">Predicting Commentaries on a Financial Report with Recurrent Neural Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Aim: The paper aims to automatically generate commentaries on financial reports. Background: Anal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Aim: The paper aims to automatically generate commentaries on financial reports. Background: Analysing and commenting financial reports is critical to evaluate the performance of a company so that management may change course to meet the targets. Generating commentaries is a task that relies on the expertise of analysts. Methodology: We propose a encoder-decoder architecture based on Recurrent Neural Networks (RNN) that are trained on both financial reports and commentaries. This architecture learns to generate those commentaries from the detected patterns on data. The proposed model is assessed on both synthetic and real data. We compare different neural network combinations on both encoder and decoder, namely GRU, LSTM and one layer neural networks. Results: The accuracy of the generated commentaries is evaluated using BLEU, ROUGE and METEOR scores and probability of commentary generation. The results show that a combination of one layer neural network and an LSTM as encoder and d...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000110"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000110"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000110; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000110]").text(description); $(".js-view-count[data-work-id=73000110]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000110; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000110']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000110, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000110]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000110,"title":"Predicting Commentaries on a Financial Report with Recurrent Neural Networks","translated_title":"","metadata":{"abstract":"Aim: The paper aims to automatically generate commentaries on financial reports. Background: Analysing and commenting financial reports is critical to evaluate the performance of a company so that management may change course to meet the targets. Generating commentaries is a task that relies on the expertise of analysts. Methodology: We propose a encoder-decoder architecture based on Recurrent Neural Networks (RNN) that are trained on both financial reports and commentaries. This architecture learns to generate those commentaries from the detected patterns on data. The proposed model is assessed on both synthetic and real data. We compare different neural network combinations on both encoder and decoder, namely GRU, LSTM and one layer neural networks. Results: The accuracy of the generated commentaries is evaluated using BLEU, ROUGE and METEOR scores and probability of commentary generation. The results show that a combination of one layer neural network and an LSTM as encoder and d...","publisher":"Canadian Conference on AI","publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":"Aim: The paper aims to automatically generate commentaries on financial reports. Background: Analysing and commenting financial reports is critical to evaluate the performance of a company so that management may change course to meet the targets. Generating commentaries is a task that relies on the expertise of analysts. Methodology: We propose a encoder-decoder architecture based on Recurrent Neural Networks (RNN) that are trained on both financial reports and commentaries. This architecture learns to generate those commentaries from the detected patterns on data. The proposed model is assessed on both synthetic and real data. We compare different neural network combinations on both encoder and decoder, namely GRU, LSTM and one layer neural networks. Results: The accuracy of the generated commentaries is evaluated using BLEU, ROUGE and METEOR scores and probability of commentary generation. The results show that a combination of one layer neural network and an LSTM as encoder and d...","internal_url":"https://www.academia.edu/73000110/Predicting_Commentaries_on_a_Financial_Report_with_Recurrent_Neural_Networks","translated_internal_url":"","created_at":"2022-03-04T00:19:23.436-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Predicting_Commentaries_on_a_Financial_Report_with_Recurrent_Neural_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":18227786,"url":"https://johnmaidens.com/papers/2019CanadianAI.pdf"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000108"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000108/Confirmation_Bias_in_Software_Development_and_Testing_An_Analysis_of_the_Effects_of_Company_Size_Experience_and_Reasoning_Skills"><img alt="Research paper thumbnail of Confirmation Bias in Software Development and Testing: An Analysis of the Effects of Company Size, Experience and Reasoning Skills" class="work-thumbnail" src="https://attachments.academia-assets.com/81696797/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000108/Confirmation_Bias_in_Software_Development_and_Testing_An_Analysis_of_the_Effects_of_Company_Size_Experience_and_Reasoning_Skills">Confirmation Bias in Software Development and Testing: An Analysis of the Effects of Company Size, Experience and Reasoning Skills</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">During all levels of software testing, the goal should be to fail the code to discover software d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">During all levels of software testing, the goal should be to fail the code to discover software defects and hence to increase software quality. However, software developers and testers are more likely to choose positive tests rather than negative ones. This is due to the phenomenon called confirmation bias which is defined as the tendency to verify one鈥檚 own hypotheses rather than trying to refute them. In this work, we aimed at identifying the factors that may affect confirmation bias levels of software developers and testers. We have investigated the effects of company size, experience and reasoning skills on bias levels. We prepared pen-and-paper and interactive tests based on two tasks from cognitive psychology literature. During pen-and-paper test, subjects had to test given hypotheses, whereas interactive test required both hypotheses generation and testing. These tests were conducted on employees of one large scale telecommunications company, three small and medium scale soft...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d7a34274dd695eeaad8f9bbd04bb6178" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696797,"asset_id":73000108,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696797/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000108"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000108"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000108; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000108]").text(description); $(".js-view-count[data-work-id=73000108]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000108; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000108']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000108, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d7a34274dd695eeaad8f9bbd04bb6178" } } $('.js-work-strip[data-work-id=73000108]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000108,"title":"Confirmation Bias in Software Development and Testing: An Analysis of the Effects of Company Size, Experience and Reasoning Skills","translated_title":"","metadata":{"abstract":"During all levels of software testing, the goal should be to fail the code to discover software defects and hence to increase software quality. However, software developers and testers are more likely to choose positive tests rather than negative ones. This is due to the phenomenon called confirmation bias which is defined as the tendency to verify one鈥檚 own hypotheses rather than trying to refute them. In this work, we aimed at identifying the factors that may affect confirmation bias levels of software developers and testers. We have investigated the effects of company size, experience and reasoning skills on bias levels. We prepared pen-and-paper and interactive tests based on two tasks from cognitive psychology literature. During pen-and-paper test, subjects had to test given hypotheses, whereas interactive test required both hypotheses generation and testing. These tests were conducted on employees of one large scale telecommunications company, three small and medium scale soft...","publisher":"PPIG","publication_date":{"day":null,"month":null,"year":2010,"errors":{}}},"translated_abstract":"During all levels of software testing, the goal should be to fail the code to discover software defects and hence to increase software quality. However, software developers and testers are more likely to choose positive tests rather than negative ones. This is due to the phenomenon called confirmation bias which is defined as the tendency to verify one鈥檚 own hypotheses rather than trying to refute them. In this work, we aimed at identifying the factors that may affect confirmation bias levels of software developers and testers. We have investigated the effects of company size, experience and reasoning skills on bias levels. We prepared pen-and-paper and interactive tests based on two tasks from cognitive psychology literature. During pen-and-paper test, subjects had to test given hypotheses, whereas interactive test required both hypotheses generation and testing. These tests were conducted on employees of one large scale telecommunications company, three small and medium scale soft...","internal_url":"https://www.academia.edu/73000108/Confirmation_Bias_in_Software_Development_and_Testing_An_Analysis_of_the_Effects_of_Company_Size_Experience_and_Reasoning_Skills","translated_internal_url":"","created_at":"2022-03-04T00:19:22.757-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696797,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696797/thumbnails/1.jpg","file_name":"PPIG2010.pdf","download_url":"https://www.academia.edu/attachments/81696797/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Confirmation_Bias_in_Software_Developmen.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696797/PPIG2010-libre.pdf?1646469779=\u0026response-content-disposition=attachment%3B+filename%3DConfirmation_Bias_in_Software_Developmen.pdf\u0026Expires=1732998484\u0026Signature=R6UwxV3frmkfGk1PqlzZbU776DRADG0bX0fIoQzoG4u2iz-3ckZx18mW1AQ14xFlCgYWfzs0xGC9gneYEaF2ngMc7aeI15wzctmLd90UTxkIbL~0ylTC4FenRSCJMQG-ANWhY4hVmI7CU8eoS6EGW4QYBEZABBi5PDLOZkPAZRMW4e3nOIviUoRmsjPvyPy4h~q6NUyaPRTiPr7C3pNfmMzImMGh7TiunAHaEWxmzlvHX7PCLKq1v6Y4YioZTasbDb58MJ2aWnjVNvxR0qQhTOkb2EDOg6I8XWIeUQ965DRRFXhHGnW8bn0bjbMy~kpxS3lGNbuBOVcy6jgrJTr1zA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Confirmation_Bias_in_Software_Development_and_Testing_An_Analysis_of_the_Effects_of_Company_Size_Experience_and_Reasoning_Skills","translated_slug":"","page_count":16,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696797,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696797/thumbnails/1.jpg","file_name":"PPIG2010.pdf","download_url":"https://www.academia.edu/attachments/81696797/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Confirmation_Bias_in_Software_Developmen.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696797/PPIG2010-libre.pdf?1646469779=\u0026response-content-disposition=attachment%3B+filename%3DConfirmation_Bias_in_Software_Developmen.pdf\u0026Expires=1732998484\u0026Signature=R6UwxV3frmkfGk1PqlzZbU776DRADG0bX0fIoQzoG4u2iz-3ckZx18mW1AQ14xFlCgYWfzs0xGC9gneYEaF2ngMc7aeI15wzctmLd90UTxkIbL~0ylTC4FenRSCJMQG-ANWhY4hVmI7CU8eoS6EGW4QYBEZABBi5PDLOZkPAZRMW4e3nOIviUoRmsjPvyPy4h~q6NUyaPRTiPr7C3pNfmMzImMGh7TiunAHaEWxmzlvHX7PCLKq1v6Y4YioZTasbDb58MJ2aWnjVNvxR0qQhTOkb2EDOg6I8XWIeUQ965DRRFXhHGnW8bn0bjbMy~kpxS3lGNbuBOVcy6jgrJTr1zA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":4761,"name":"Software Testing","url":"https://www.academia.edu/Documents/in/Software_Testing"},{"id":135988,"name":"Confirmation bias","url":"https://www.academia.edu/Documents/in/Confirmation_bias"}],"urls":[{"id":18227784,"url":"http://oro.open.ac.uk/45370/1/PPIG2010.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000107"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000107/Part_II_Evaluation_Field_Studies"><img alt="Research paper thumbnail of Part II. Evaluation: Field Studies" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000107/Part_II_Evaluation_Field_Studies">Part II. Evaluation: Field Studies</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One way to implement and evaluate the effectiveness of recommendation systems for software engine...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One way to implement and evaluate the effectiveness of recommendation systems for software engineering is to conduct field studies. Field studies are important as they are the extension of the laboratory experiments into real life situations of organizations and/or society. They bring greater realism to the phenomenon that is under study. However, field studies require following a rigorous research approach with many challenges attached, such as difficulties in implementing the research design, achieving sufficient control, replication, validity and reliability. In practice, another challenge is to find organizations who are prepared to be experimented on. In this chapter, we provide details regarding step-by-step process in the construction and deployment of recommendation systems for software engineering in the field. We also emphasize three main challenges (organizational, data, design) encountered during field studies in both general and specific to software organizations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000107"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000107"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000107; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000107]").text(description); $(".js-view-count[data-work-id=73000107]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000107; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000107']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000107, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000107]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000107,"title":"Part II. Evaluation: Field Studies","translated_title":"","metadata":{"abstract":"One way to implement and evaluate the effectiveness of recommendation systems for software engineering is to conduct field studies. Field studies are important as they are the extension of the laboratory experiments into real life situations of organizations and/or society. They bring greater realism to the phenomenon that is under study. However, field studies require following a rigorous research approach with many challenges attached, such as difficulties in implementing the research design, achieving sufficient control, replication, validity and reliability. In practice, another challenge is to find organizations who are prepared to be experimented on. In this chapter, we provide details regarding step-by-step process in the construction and deployment of recommendation systems for software engineering in the field. We also emphasize three main challenges (organizational, data, design) encountered during field studies in both general and specific to software organizations.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}}},"translated_abstract":"One way to implement and evaluate the effectiveness of recommendation systems for software engineering is to conduct field studies. Field studies are important as they are the extension of the laboratory experiments into real life situations of organizations and/or society. They bring greater realism to the phenomenon that is under study. However, field studies require following a rigorous research approach with many challenges attached, such as difficulties in implementing the research design, achieving sufficient control, replication, validity and reliability. In practice, another challenge is to find organizations who are prepared to be experimented on. In this chapter, we provide details regarding step-by-step process in the construction and deployment of recommendation systems for software engineering in the field. We also emphasize three main challenges (organizational, data, design) encountered during field studies in both general and specific to software organizations.","internal_url":"https://www.academia.edu/73000107/Part_II_Evaluation_Field_Studies","translated_internal_url":"","created_at":"2022-03-04T00:19:22.551-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Part_II_Evaluation_Field_Studies","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000105"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000105/Applications_of_Feature_Selection_Techniques_on_Large_Biomedical_Datasets"><img alt="Research paper thumbnail of Applications of Feature Selection Techniques on Large Biomedical Datasets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000105/Applications_of_Feature_Selection_Techniques_on_Large_Biomedical_Datasets">Applications of Feature Selection Techniques on Large Biomedical Datasets</a></div><div class="wp-workCard_item"><span>Advances in Artificial Intelligence</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The main goal of this paper is to determine the best feature selection algorithm to use on large ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The main goal of this paper is to determine the best feature selection algorithm to use on large biomedical datasets. Feature Selection shows a potential role in analyzing large biomedical datasets. Four different feature selection techniques have been employed on large biomedical datasets. These techniques were Information Gain, Chi-Squared, Markov Blanket Discovery, and Recursive Feature Elimination. We measured the efficiency of the selection, the stability of the algorithms, and the quality of the features chosen. Of the four techniques used, the Information Gain and Chi-Squared filters were the most efficient and stable. Both Markov Blanket Discovery and Recursive Feature Elimination took significantly longer to select features, and were less stable. The features selected by Recursive Feature Elimination were of the highest quality, followed by Information Gain and Chi-Squared, and Markov Blanket Discovery placed last. For the purpose of education (e.g. those in the biomedical field teaching data techniques), we recommend Information Gain or Chi-Squared filter. For the purpose of research or analyzing, we recommend one of the filters or Recursive Feature Elimination, depending on the situation. We do not recommend the use of Markov Blanket discovery for the situations used in this trial, keeping in mind that the experiments were not exhaustive.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000105"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000105"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000105; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000105]").text(description); $(".js-view-count[data-work-id=73000105]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000105; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000105']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000105, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000105]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000105,"title":"Applications of Feature Selection Techniques on Large Biomedical Datasets","translated_title":"","metadata":{"abstract":"The main goal of this paper is to determine the best feature selection algorithm to use on large biomedical datasets. Feature Selection shows a potential role in analyzing large biomedical datasets. Four different feature selection techniques have been employed on large biomedical datasets. These techniques were Information Gain, Chi-Squared, Markov Blanket Discovery, and Recursive Feature Elimination. We measured the efficiency of the selection, the stability of the algorithms, and the quality of the features chosen. Of the four techniques used, the Information Gain and Chi-Squared filters were the most efficient and stable. Both Markov Blanket Discovery and Recursive Feature Elimination took significantly longer to select features, and were less stable. The features selected by Recursive Feature Elimination were of the highest quality, followed by Information Gain and Chi-Squared, and Markov Blanket Discovery placed last. For the purpose of education (e.g. those in the biomedical field teaching data techniques), we recommend Information Gain or Chi-Squared filter. For the purpose of research or analyzing, we recommend one of the filters or Recursive Feature Elimination, depending on the situation. We do not recommend the use of Markov Blanket discovery for the situations used in this trial, keeping in mind that the experiments were not exhaustive.","publisher":"Springer International Publishing","publication_name":"Advances in Artificial Intelligence"},"translated_abstract":"The main goal of this paper is to determine the best feature selection algorithm to use on large biomedical datasets. Feature Selection shows a potential role in analyzing large biomedical datasets. Four different feature selection techniques have been employed on large biomedical datasets. These techniques were Information Gain, Chi-Squared, Markov Blanket Discovery, and Recursive Feature Elimination. We measured the efficiency of the selection, the stability of the algorithms, and the quality of the features chosen. Of the four techniques used, the Information Gain and Chi-Squared filters were the most efficient and stable. Both Markov Blanket Discovery and Recursive Feature Elimination took significantly longer to select features, and were less stable. The features selected by Recursive Feature Elimination were of the highest quality, followed by Information Gain and Chi-Squared, and Markov Blanket Discovery placed last. For the purpose of education (e.g. those in the biomedical field teaching data techniques), we recommend Information Gain or Chi-Squared filter. For the purpose of research or analyzing, we recommend one of the filters or Recursive Feature Elimination, depending on the situation. We do not recommend the use of Markov Blanket discovery for the situations used in this trial, keeping in mind that the experiments were not exhaustive.","internal_url":"https://www.academia.edu/73000105/Applications_of_Feature_Selection_Techniques_on_Large_Biomedical_Datasets","translated_internal_url":"","created_at":"2022-03-04T00:19:22.298-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Applications_of_Feature_Selection_Techniques_on_Large_Biomedical_Datasets","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"}],"urls":[{"id":18227783,"url":"http://link.springer.com/content/pdf/10.1007/978-3-030-18305-9_57"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000104"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000104/Deep_Super_Learner_A_Deep_Ensemble_for_Classification_Problems"><img alt="Research paper thumbnail of Deep Super Learner: A Deep Ensemble for Classification Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/81696794/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000104/Deep_Super_Learner_A_Deep_Ensemble_for_Classification_Problems">Deep Super Learner: A Deep Ensemble for Classification Problems</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="da3d75afd9dee068b8040582959f6c06" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696794,"asset_id":73000104,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696794/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000104"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000104"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000104; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000104]").text(description); $(".js-view-count[data-work-id=73000104]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000104; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000104']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000104, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "da3d75afd9dee068b8040582959f6c06" } } $('.js-work-strip[data-work-id=73000104]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000104,"title":"Deep Super Learner: A Deep Ensemble for Classification Problems","translated_title":"","metadata":{"publisher":"Springer International Publishing","grobid_abstract":"Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datasets. While traditional machine learning algorithms can address these drawbacks, they are not typically capable of the performance levels achieved by deep neural networks. To improve performance, ensemble methods are used to combine multiple base learners. Super learning is an ensemble that finds the optimal combination of diverse learning algorithms. This paper proposes deep super learning as an approach which achieves log loss and accuracy results competitive to deep neural networks while employing traditional machine learning algorithms in a hierarchical structure. The deep super learner is flexible, adaptable, and easy to train with good performance across different tasks using identical hyper-parameter values. Using traditional machine learning requires fewer hyper-parameters, allows transparency into results, and has relatively fast convergence on smaller datasets. Experimental results show that the deep super learner has superior performance compared to the individual base learners, single-layer ensembles, and in some cases deep neural networks. Performance of the deep super learner may further be improved with task-specific tuning.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Lecture Notes in Computer Science","grobid_abstract_attachment_id":81696794},"translated_abstract":null,"internal_url":"https://www.academia.edu/73000104/Deep_Super_Learner_A_Deep_Ensemble_for_Classification_Problems","translated_internal_url":"","created_at":"2022-03-04T00:19:22.047-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":144523662,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":81696794,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696794/thumbnails/1.jpg","file_name":"1803.pdf","download_url":"https://www.academia.edu/attachments/81696794/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Deep_Super_Learner_A_Deep_Ensemble_for_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696794/1803-libre.pdf?1646469781=\u0026response-content-disposition=attachment%3B+filename%3DDeep_Super_Learner_A_Deep_Ensemble_for_C.pdf\u0026Expires=1732998484\u0026Signature=WJosHw098afbqssCxTHKW5oml8SMJaVvMi6axWg-Uo8NylwsvBEZPAJMUTCAa6gR6V6MPvl3lC-2ueFSiPFBFKtlUiZLXeLZi70lVaDTKNS04K1RtRbMWNKMf5EqqBcqwU3mBHG6q5k65Y4oYnzGcsO3wvupEWrv1nrdKqgOUTNEH2P9lKvT0j1GYVF3tQHI7i-UJUvunFHzNtEoiJMdflMUWQH0xK4KqR5R9Zw22yVVJruvsAtKqvgSBBJipoJ2JscrbNfUKmKew9QbuDTa2bhzu3oVM0Y7Zt7rYSrKQYDicdrMB7lvcIet3eEf0GLSg~9vOJsvvRzuKkVAu0GU3g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Deep_Super_Learner_A_Deep_Ensemble_for_Classification_Problems","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":144523662,"first_name":"Ayse","middle_initials":null,"last_name":"Bener","page_name":"BenerAyse","domain_name":"independent","created_at":"2020-02-03T23:13:28.918-08:00","display_name":"Ayse Bener","url":"https://independent.academia.edu/BenerAyse"},"attachments":[{"id":81696794,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696794/thumbnails/1.jpg","file_name":"1803.pdf","download_url":"https://www.academia.edu/attachments/81696794/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Deep_Super_Learner_A_Deep_Ensemble_for_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696794/1803-libre.pdf?1646469781=\u0026response-content-disposition=attachment%3B+filename%3DDeep_Super_Learner_A_Deep_Ensemble_for_C.pdf\u0026Expires=1732998484\u0026Signature=WJosHw098afbqssCxTHKW5oml8SMJaVvMi6axWg-Uo8NylwsvBEZPAJMUTCAa6gR6V6MPvl3lC-2ueFSiPFBFKtlUiZLXeLZi70lVaDTKNS04K1RtRbMWNKMf5EqqBcqwU3mBHG6q5k65Y4oYnzGcsO3wvupEWrv1nrdKqgOUTNEH2P9lKvT0j1GYVF3tQHI7i-UJUvunFHzNtEoiJMdflMUWQH0xK4KqR5R9Zw22yVVJruvsAtKqvgSBBJipoJ2JscrbNfUKmKew9QbuDTa2bhzu3oVM0Y7Zt7rYSrKQYDicdrMB7lvcIet3eEf0GLSg~9vOJsvvRzuKkVAu0GU3g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":81696795,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81696795/thumbnails/1.jpg","file_name":"1803.pdf","download_url":"https://www.academia.edu/attachments/81696795/download_file","bulk_download_file_name":"Deep_Super_Learner_A_Deep_Ensemble_for_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81696795/1803-libre.pdf?1646469780=\u0026response-content-disposition=attachment%3B+filename%3DDeep_Super_Learner_A_Deep_Ensemble_for_C.pdf\u0026Expires=1732998484\u0026Signature=AUFRCOCw~ROasDBsdOsZCQefF3OBXJDje~EDzcDIumulMYwtbET6BgHH5hW7jN3TTul1kQjERF9w7wkUQ8edUr-PkBq-X6vFN~~G-r67ULbChnLzwuN4D3zWLrnugiiqmO~ZwZy-jXdwBMOEBqB2LJNoio5EyfXaL6FOGcRBI7yQnEX925h2TdGxFXhm76O00~aiES7wLGeQCGgGgRkJgD0h2DO1w7GMNSFDT6LfgarUuPAQ0ANL6UZo3OWuFNEIrzA1P3HSqsEZ~ct0QwYxPqufRJMBtVhNg~5XbDSiRvz2CkU~EM1Pf24J1bI7VaKU6WngpSQp8TCcUNy-3SQg~A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":18227782,"url":"http://arxiv.org/pdf/1803.02323"}]}, dispatcherData: dispatcherData }); 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Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bb821be64a195fb4f258c67c6b7299e1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81696792,"asset_id":73000103,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81696792/download_file?st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&st=MTczMjk5NDg4NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000103"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000103"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000103; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000103]").text(description); $(".js-view-count[data-work-id=73000103]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000103; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000103']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000103, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "bb821be64a195fb4f258c67c6b7299e1" } } $('.js-work-strip[data-work-id=73000103]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000103,"title":"AI-Based Software Defect Predictors: Applications and Benefits in a Case Study","translated_title":"","metadata":{"abstract":"Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. 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Furthermore, we compared the performance of our model with that of another testing strategy applied in...","publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","publication_name":"AI Magazine"},"translated_abstract":"Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="73000102"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/73000102/Practice_does_not_always_make_perfect_need_for_selection_curricula_in_modern_surgical_training"><img alt="Research paper thumbnail of Practice does not always make perfect: need for selection curricula in modern surgical training" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/73000102/Practice_does_not_always_make_perfect_need_for_selection_curricula_in_modern_surgical_training">Practice does not always make perfect: need for selection curricula in modern surgical training</a></div><div class="wp-workCard_item"><span>Surgical Endoscopy</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is hypothesized that not all surgical trainees are able to reach technical competence despite ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is hypothesized that not all surgical trainees are able to reach technical competence despite ongoing practice. The objectives of the study were to assess a trainees&amp;amp;amp;amp;amp;#39; ability to reach technical competence by assessing learning patterns of the acquisition of surgical skills. Furthermore, it aims to determine whether individuals&amp;amp;amp;amp;amp;#39; learning patterns were consistent across a range of open and laparoscopic tasks of variable difficulty. Sixty-five preclinical medical students participated in a training curriculum with standardized feedback over forty repetitions of the following laparoscopic and open technical tasks: peg transfer (PT), circle cutting (CC), intracorporeal knot tie (IKT), one-handed tie, and simulated laparotomy closure. Data mining techniques were used to analyze the prospectively collected data and stratify the students into four learning clusters. Performance was compared between groups, and learning curve characteristics unique to trainees who have difficulty reaching technical competence were quantified. Top performers (22-35%) and high performers (32-42%) reached proficiency in all tasks. Moderate performers (25-37%) reached proficiency for all open tasks but not all laparoscopic tasks. Low performers (8-15%) failed to reach proficiency in four of five tasks including all laparoscopic tasks (PT 7.8%; CC 9.4%; IKT 15.6%). Participants in lower performance clusters demonstrated sustained performance disadvantage across tasks, with widely variable learning curves and no evidence of progression towards a plateau phase. Most students reached proficiency across a range of surgical tasks, but low-performing trainees failed to reach competence in laparoscopic tasks. With increasing use of laparoscopy in surgical practice, screening potential candidates to identify the lowest performers may be beneficial.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="73000102"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="73000102"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 73000102; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=73000102]").text(description); $(".js-view-count[data-work-id=73000102]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 73000102; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='73000102']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 73000102, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=73000102]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":73000102,"title":"Practice does not always make perfect: need for selection curricula in modern surgical training","translated_title":"","metadata":{"abstract":"It is hypothesized that not all surgical trainees are able to reach technical competence despite ongoing practice. The objectives of the study were to assess a trainees\u0026amp;amp;amp;amp;amp;#39; ability to reach technical competence by assessing learning patterns of the acquisition of surgical skills. Furthermore, it aims to determine whether individuals\u0026amp;amp;amp;amp;amp;#39; learning patterns were consistent across a range of open and laparoscopic tasks of variable difficulty. Sixty-five preclinical medical students participated in a training curriculum with standardized feedback over forty repetitions of the following laparoscopic and open technical tasks: peg transfer (PT), circle cutting (CC), intracorporeal knot tie (IKT), one-handed tie, and simulated laparotomy closure. Data mining techniques were used to analyze the prospectively collected data and stratify the students into four learning clusters. Performance was compared between groups, and learning curve characteristics unique to trainees who have difficulty reaching technical competence were quantified. 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