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Running the components of these huge projects for testing purposes might be a costly process; therefore, parameter estimation can be used instead. Software defect" /> <meta name="twitter:image" content="http://a.academia-assets.com/images/twitter-card.jpeg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/124273809/The_Effect_of_the_Dataset_Size_on_the_Accuracy_of_Software_Defect_Prediction_Models_An_Empirical_Study" /> <meta property="og:title" content="The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="The ongoing development of computer systems requires massive software projects. Running the components of these huge projects for testing purposes might be a costly process; therefore, parameter estimation can be used instead. Software defect" /> <meta property="article:author" content="https://ua-huntsville.academia.edu/MohammadAlshayeb" /> <meta name="description" content="The ongoing development of computer systems requires massive software projects. Running the components of these huge projects for testing purposes might be a costly process; therefore, parameter estimation can be used instead. Software defect" /> <title>(PDF) The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study | Mohammad Alshayeb - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/124273809/The_Effect_of_the_Dataset_Size_on_the_Accuracy_of_Software_Defect_Prediction_Models_An_Empirical_Study" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '49879c2402910372f4abc62630a427bbe033d190'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1732411404000); window.Aedu.timeDifference = new Date().getTime() - 1732411404000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"The ongoing development of computer systems requires massive software projects. Running the components of these huge projects for testing purposes might be a costly process; therefore, parameter estimation can be used instead. Software defect prediction models are crucial for software quality assurance. This study investigates the impact of dataset size and feature selection algorithms on software defect prediction models. We use two approaches to build software defect prediction models: a statistical approach and a machine learning approach with support vector machines (SVMs). The fault prediction model was built based on four datasets of different sizes. Additionally, four feature selection algorithms were used. We found that applying the SVM defect prediction model on datasets with a reduced number of measures as features may enhance the accuracy of the fault prediction model. Also, it directs the test effort to maintain the most influential set of metrics. We also found that the...","author":[{"@context":"https://schema.org","@type":"Person","name":"Mohammad Alshayeb"}],"contributor":[],"dateCreated":"2024-09-29","dateModified":"2024-09-29","datePublished":"2021-01-01","headline":"The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study","inLanguage":"en","keywords":["Computer Science","Machine Learning","Data Mining","Feature Selection","Software","Predictive Modelling","Inteligencia artificial","Support vector machine"],"locationCreated":null,"publication":"Inteligencia Artificial","publisher":{"@context":"https://schema.org","@type":"Organization","name":"IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial"},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/124273809/The_Effect_of_the_Dataset_Size_on_the_Accuracy_of_Software_Defect_Prediction_Models_An_Empirical_Study","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"ua-huntsville"}]}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/single_work_page/loswp-352e32ba4e89304dc0b4fa5b3952eef2198174c54cdb79066bc62e91c68a1a91.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-8d679e925718b5e8e4b18e9a4fab37f7eaa99e43386459376559080ac8f2856a.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-3cea6e0ad4715ed965c49bfb15dedfc632787b32ff6d8c3a474182b231146ab7.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/text_button-73590134e40cdb49f9abdc8e796cc00dc362693f3f0f6137d6cf9bb78c318ce7.css" /><link crossorigin="" 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Running the components of these huge projects for testing purposes might be a costly process; therefore, parameter estimation can be used instead. Software defect prediction models are crucial for software quality assurance. This study investigates the impact of dataset size and feature selection algorithms on software defect prediction models. We use two approaches to build software defect prediction models: a statistical approach and a machine learning approach with support vector machines (SVMs). The fault prediction model was built based on four datasets of different sizes. Additionally, four feature selection algorithms were used. We found that applying the SVM defect prediction model on datasets with a reduced number of measures as features may enhance the accuracy of the fault prediction model. Also, it directs the test effort to maintain the most influential set of metrics. We also found that the...","publisher":"IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial","publication_date":"2021,,","publication_name":"Inteligencia Artificial"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [169048435]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":118530691,"attachmentType":"pdf"}"><img alt="First page of “The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/118530691/mini_magick20240930-1-uymtf4.png?1727674930" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="169048435" href="https://ua-huntsville.academia.edu/MohammadAlshayeb"><img alt="Profile image of Mohammad Alshayeb" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Mohammad Alshayeb</a></div><p class="ds-work-card--detail ds2-5-body-sm">2021, Inteligencia Artificial</p><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">The ongoing development of computer systems requires massive software projects. Running the components of these huge projects for testing purposes might be a costly process; therefore, parameter estimation can be used instead. Software defect prediction models are crucial for software quality assurance. This study investigates the impact of dataset size and feature selection algorithms on software defect prediction models. We use two approaches to build software defect prediction models: a statistical approach and a machine learning approach with support vector machines (SVMs). The fault prediction model was built based on four datasets of different sizes. Additionally, four feature selection algorithms were used. We found that applying the SVM defect prediction model on datasets with a reduced number of measures as features may enhance the accuracy of the fault prediction model. Also, it directs the test effort to maintain the most influential set of metrics. We also found that the...</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":118530691,"attachmentType":"pdf","workUrl":"https://www.academia.edu/124273809/The_Effect_of_the_Dataset_Size_on_the_Accuracy_of_Software_Defect_Prediction_Models_An_Empirical_Study"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":118530691,"attachmentType":"pdf","workUrl":"https://www.academia.edu/124273809/The_Effect_of_the_Dataset_Size_on_the_Accuracy_of_Software_Defect_Prediction_Models_An_Empirical_Study"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="118530691" data-landing_url="https://www.academia.edu/124273809/The_Effect_of_the_Dataset_Size_on_the_Accuracy_of_Software_Defect_Prediction_Models_An_Empirical_Study" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="79616709" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/79616709/A_Feature_Selection_Based_Model_for_Software_Defect_Prediction">A Feature Selection Based Model for Software Defect Prediction</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" 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Support Vector Machine with Recursive Feature Elimination for both Logistic Regression and Random Forest was introduced to evaluate the performance between filter, wrapper, and embedded feature select...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Survey of Feature Selection Methods for Software Defect Prediction Models","attachmentId":77993725,"attachmentType":"pdf","work_url":"https://www.academia.edu/67013502/A_Survey_of_Feature_Selection_Methods_for_Software_Defect_Prediction_Models","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/67013502/A_Survey_of_Feature_Selection_Methods_for_Software_Defect_Prediction_Models"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="52950165" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/52950165/Impact_of_Feature_Selection_Methods_on_the_Predictive_Performance_of_Software_Defect_Prediction_Models_An_Extensive_Empirical_Study">Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="49949400" href="https://unilorin.academia.edu/AbdullateefBalogun">Abdullateef Balogun</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Symmetry</p><p class="ds-related-work--abstract ds2-5-body-sm">Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and many FS methods have been proposed in the context of software defect prediction (SDP). 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Machines","attachmentId":81322867,"attachmentType":"pdf","work_url":"https://www.academia.edu/72369631/Software_Fault_Proneness_Prediction_Using_Support_Vector_Machines","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/72369631/Software_Fault_Proneness_Prediction_Using_Support_Vector_Machines"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="34314424" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/34314424/Predicting_Software_Defects_through_SVM_An_Empirical_Approach">Predicting Software Defects through SVM: An Empirical Approach</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="59400872" href="https://amu-in.academia.edu/JunaidReshi">Junaid A Reshi</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Software defect prediction is an important aspect of preventive maintenance of a software. Many techniques have been employed to improve software quality through defect prediction. This paper introduces an approach of defect prediction through a machine learning algorithm, support vector machines (SVM), by using the code smells as the factor. Smell prediction model based on support vector machines was used to predict defects in the subsequent releases of the eclipse software. The results signify the role of smells in predicting the defects of a software. The results can further be used as a baseline to investigate further the role of smells in predicting defects.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Predicting Software Defects through SVM: An Empirical Approach","attachmentId":54215255,"attachmentType":"pdf","work_url":"https://www.academia.edu/34314424/Predicting_Software_Defects_through_SVM_An_Empirical_Approach","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/34314424/Predicting_Software_Defects_through_SVM_An_Empirical_Approach"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="44558609" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/44558609/Performance_comparison_of_Machine_learning_classifiers_in_Software_Defects_Prediction">Performance comparison of Machine learning classifiers in Software Defects Prediction</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2594470" href="https://independent.academia.edu/iosrjournals">IOSR Journals</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Background: In software development life cycle, software testing is the main stage which can minimize the defects of software. A domain which has receiving much attention of software researchers since past couple of years is software defects prediction (SDP). Its aim to minimize the cost, time and improve the efficiency of software. The main aim of this research is to show a comparative analysis of software defect prediction based on support vector machine SVM and extreme learning machine ELM. In this domain defect prediction models were created using three different prediction techniques based on test data and training data. i.e. cross-validation prediction, cross-version prediction and cross-project prediction. In this study we used cross version prediction approach, data from old version of a software is used as training data to develop the prediction model and the model is evaluated from same project of current version. Materials and Methods: In our studies, we consider three different versions of eclipse version control system then we had split the data into training and tested sets. We choose different object oriented metrics and algorithm to build our model, aiming to predict software defects in different versions. For training purpose of our model we used SVM and ELM. To validate our prediction models, we can calculate the performance of prediction model using some popular used measurement scales such as accuracy, precision, recall, AUC (Area under ROC curve). Results: By comparing the file based results of SVM and ELM we can find the average accuracy values and AUC. This means the extreme learning machine has the highest AUC value, but the value of accuracy is also close to SVM. And SVM have similar accuracy, and very close AUC value. Then we can see how these models perform in package based prediction. By comparing the data in package based prediction of SVM and ELM, the accuracy and AUC values shows thatSVM has best accuracy, but the value of AUC decreases apparently. So we can conclude that SVM has best prediction results in file based defects.The results demonstrate that support vector machine is best fit for the cross version defect prediction. Conclusion: Software testing has become more and more important in software reliability since last couple of years. But on software testing we are wasting much time, resource and money. Software defect prediction can help to improve the efficiency of software testing and guide the direct resource allocation. In this study, we discussed the key techniques including software metrics, classifiers, and defect prediction models and its evaluation.Python language is most widely use language especially in data science.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Performance comparison of Machine learning classifiers in Software Defects Prediction","attachmentId":65011074,"attachmentType":"pdf","work_url":"https://www.academia.edu/44558609/Performance_comparison_of_Machine_learning_classifiers_in_Software_Defects_Prediction","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/44558609/Performance_comparison_of_Machine_learning_classifiers_in_Software_Defects_Prediction"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="87379733" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/87379733/The_impact_of_training_data_selection_on_the_software_defect_prediction_performance_and_data_complexity">The impact of training data selection on the software defect prediction performance and data complexity</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="375470" href="https://utem.academia.edu/SabrinaAhmad">Sabrina Ahmad</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="238845534" href="https://independent.academia.edu/BenyaminLangguSinaga">Benyamin Langgu Sinaga</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="171721299" href="https://independent.academia.edu/beeiiaes">beei iaes</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Bulletin of Electrical Engineering and Informatics, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Directly learning a defect prediction model from cross-project datasets results in a model with poor performance. Hence, training data selection becomes a feasible solution to this problem. Limited comparative studies investigating the effect of training data selection on the prediction performance have presented contradictory results. Those studies also did not analyze why a training data selection method underperforms. This study aims to investigate the impact of training data selection on the defect prediction model and data complexity measures. The method is based on an empirical comparison between prediction performance and data complexity measure before and after selection. This study compared 13 training data selection methods on 61 projects using six classification algorithms and measured the data complexity using six complexity measures focusing on overlap class, noise level, and class imbalanced ratio. Experimental results indicate that the best method for each dataset varies depending on the dataset and classifiers. The training data selection most affects noise rate and class imbalance. We concluded that critically selecting the training data method could improve the performance of the prediction model. We recommend dealing with noise and unbalanced classes when designing training data methods.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The impact of training data selection on the software defect prediction performance and data complexity","attachmentId":91604264,"attachmentType":"pdf","work_url":"https://www.academia.edu/87379733/The_impact_of_training_data_selection_on_the_software_defect_prediction_performance_and_data_complexity","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/87379733/The_impact_of_training_data_selection_on_the_software_defect_prediction_performance_and_data_complexity"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":118530691,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":118530691,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_118530691" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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