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In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. 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varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A sub...","publisher":"Hindawi Limited","publication_name":"Scientific Programming"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [395715]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; 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":117075390,"attachmentType":"pdf"}"><img alt="First page of “Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/117075390/mini_magick20240801-1-b4m9tx.png?1722533212" /><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">Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions</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="395715" href="https://suleyman-demirel.academia.edu/UtkuKose"><img alt="Profile image of Utku Köse" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/395715/124145/64827257/s65_utku.k_se.png" />Utku Köse</a></div><p class="ds-work-card--detail ds2-5-body-sm">Scientific Programming</p><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A sub...</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":117075390,"attachmentType":"pdf","workUrl":"https://www.academia.edu/122405945/Predictive_Analytics_and_Software_Defect_Severity_A_Systematic_Review_and_Future_Directions"}">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":117075390,"attachmentType":"pdf","workUrl":"https://www.academia.edu/122405945/Predictive_Analytics_and_Software_Defect_Severity_A_Systematic_Review_and_Future_Directions"}"><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="117075390" data-landing_url="https://www.academia.edu/122405945/Predictive_Analytics_and_Software_Defect_Severity_A_Systematic_Review_and_Future_Directions" 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="73619014" 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/73619014/A_Systematic_Literature_Review_of_Software_Defect_Prediction_Research_Trends_Datasets_Methods_and_Frameworks">A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks</a><div class="ds-related-work--metadata"><a 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href="https://independent.academia.edu/J%C3%B6rgenHansson">Jörgen Hansson</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Communications in Computer and Information Science, 2014</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 Adoption of Machine Learning Techniques for Software Defect Prediction: An Initial Industrial Validation","attachmentId":42811970,"attachmentType":"pdf","work_url":"https://www.academia.edu/15959620/The_Adoption_of_Machine_Learning_Techniques_for_Software_Defect_Prediction_An_Initial_Industrial_Validation","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/15959620/The_Adoption_of_Machine_Learning_Techniques_for_Software_Defect_Prediction_An_Initial_Industrial_Validation"><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="2" data-entity-id="106713490" 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/106713490/SOFTWARE_DEFECT_PREDICTION_USING_MACHINE_LEARNING_APPROACH_A_Contemporary_review">SOFTWARE DEFECT PREDICTION USING MACHINE LEARNING APPROACH : A Contemporary review</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="55583905" href="https://fountainuniversity.academia.edu/BaseeratAbdusalam">Baseerat A . Abdulsalami</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Detecting defects in software at the bleeding edge of a software development life cycle is vital. Identifying defects before the deployment of software aids in delivering high-quality products, and reduces development costs. Machine learning techniques are deployed in the earlier stages of software development to improve software performance quality and decrease software maintenance costs. This study focuses on reviewing some papers published in software defect prediction using Machine learning techniques from 2020 to the current time to determine the predominance of machine learning methodologies adoption in software defect prediction. Google Scholar was used to source research papers for this study, and data was gathered from the publications. The process involves reviewing the selected papers, writing a concise synopsis of the papers, connecting and involving them where appropriate, reviewing existing methodology, and finally summarizing the findings. The result shows recent acti...</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":"SOFTWARE DEFECT PREDICTION USING MACHINE LEARNING APPROACH : A Contemporary review","attachmentId":105764939,"attachmentType":"pdf","work_url":"https://www.academia.edu/106713490/SOFTWARE_DEFECT_PREDICTION_USING_MACHINE_LEARNING_APPROACH_A_Contemporary_review","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/106713490/SOFTWARE_DEFECT_PREDICTION_USING_MACHINE_LEARNING_APPROACH_A_Contemporary_review"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 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for Software Bug Prediction: A Systematic Review","attachmentId":76234806,"attachmentType":"pdf","work_url":"https://www.academia.edu/63994997/Machine_Learning_Techniques_for_Software_Bug_Prediction_A_Systematic_Review","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/63994997/Machine_Learning_Techniques_for_Software_Bug_Prediction_A_Systematic_Review"><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="124459477" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md 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Defect Severity Prediction Model","attachmentId":118680416,"attachmentType":"pdf","work_url":"https://www.academia.edu/124459477/Measuring_the_Impact_of_Predictive_Models_on_the_Software_Project_A_Cost_Service_Time_and_Risk_Evaluation_of_a_Metric_based_Defect_Severity_Prediction_Model","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/124459477/Measuring_the_Impact_of_Predictive_Models_on_the_Software_Project_A_Cost_Service_Time_and_Risk_Evaluation_of_a_Metric_based_Defect_Severity_Prediction_Model"><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="5" data-entity-id="40239074" 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/40239074/A_Literature_Review_Study_of_Software_Defect_Prediction_using_Machine_Learning_Techniques">A Literature Review Study of Software Defect Prediction using Machine Learning Techniques</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="28589548" href="https://bahirdar.academia.edu/ermiyasbirhanu">ermiyas birhanu</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Emerging Research in Management &Technology , 2017</p><p class="ds-related-work--abstract ds2-5-body-sm">oftware systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.</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 Literature Review Study of Software Defect Prediction using Machine Learning Techniques","attachmentId":60470917,"attachmentType":"pdf","work_url":"https://www.academia.edu/40239074/A_Literature_Review_Study_of_Software_Defect_Prediction_using_Machine_Learning_Techniques","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/40239074/A_Literature_Review_Study_of_Software_Defect_Prediction_using_Machine_Learning_Techniques"><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="6" data-entity-id="69209551" 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/69209551/Prevalence_of_Machine_Learning_Techniques_in_Software_Defect_Prediction">Prevalence of Machine Learning Techniques in 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" data-author-id="103036378" href="https://bdsongonline.academia.edu/ProfessorDrMdIsmailJabiullah">Professor Dr. Md. Ismail Jabiullah</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Software Defect Prediction (SDP) is a popular research area which plays an important role for software quality. It works as an indicator of whether a software module is defect-free or defective. In this study, a review has been conducted from January 2015 to August 2019 and 165 articles are selected in the area of SDP to know the prevalence of Machine Learning (ML) techniques. These articles are collected by searching in Google Scholar, and they are published in various platforms (e.g., IEEE, Springer, Elsevier). Firstly the information has been extracted from the collected particles, and then the information has been pre-processed, categorized, visualized, and finally, the results have been reported. The result shows the most frequently used data sets, classifiers, performance metrics, and techniques in SDP. This investigation will help to find the prevalence of ML techniques in SDP and give a quick view to understand the trends of ML techniques in defect prediction research.</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":"Prevalence of Machine Learning Techniques in Software Defect Prediction","attachmentId":79391948,"attachmentType":"pdf","work_url":"https://www.academia.edu/69209551/Prevalence_of_Machine_Learning_Techniques_in_Software_Defect_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/69209551/Prevalence_of_Machine_Learning_Techniques_in_Software_Defect_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="7" data-entity-id="17046588" 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/17046588/A_systematic_review_of_software_fault_prediction_studies">A systematic review of software fault prediction studies</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="17180161" href="https://independent.academia.edu/BanuDiri">Banu Diri</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Expert Systems with Applications, 2009</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 systematic review of software fault prediction studies","attachmentId":42344466,"attachmentType":"pdf","work_url":"https://www.academia.edu/17046588/A_systematic_review_of_software_fault_prediction_studies","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/17046588/A_systematic_review_of_software_fault_prediction_studies"><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="50798644" 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/50798644/SOFTWARE_DEFECT_PREDICTION_PAST_PRESENT_AND_FUTURE">SOFTWARE DEFECT PREDICTION: PAST PRESENT AND FUTURE</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="39122404" href="https://iaeme.academia.edu/publication">IAEME Publication</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IAEME PUBLICATION, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">Software development calls for several defect prediction methodologies using critical parameters such as review effort measurement, test effort estimation, phase gate containment, change request cost, re-usability, size and quality to improve the quality of deliverables. Nonetheless, a lot of these methodologies are actually in development stages and further research is required to produce a strong and dependable model. Many research centers have started more research projects in these research areas. Through this study, we investigated research papers and categorized depending on the importance to user community. We conducted a survey on a software application defect prediction methodologies based on machine learning approaches as well as statistical approaches. This paper contains an outline of works that have been published so far and not a comprehensive review of all the papers published on the topic. We’re confident that the survey of ours will help researchers to under- stand developments in this particular field of study in an effective and easy manner. We have also introduced as well as discussed the latest trends in defect prediction.</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":"SOFTWARE DEFECT PREDICTION: PAST PRESENT AND FUTURE","attachmentId":68665160,"attachmentType":"pdf","work_url":"https://www.academia.edu/50798644/SOFTWARE_DEFECT_PREDICTION_PAST_PRESENT_AND_FUTURE","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/50798644/SOFTWARE_DEFECT_PREDICTION_PAST_PRESENT_AND_FUTURE"><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="106747693" 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/106747693/A_Systematic_Approach_for_Enhancing_Software_Defect_Prediction_Using_Machine_Learning">A Systematic Approach for Enhancing Software Defect Prediction Using Machine Learning</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="13407718" href="https://green.academia.edu/MdSolaimanMia">Md. Solaiman Mia</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM)</p><p class="ds-related-work--abstract ds2-5-body-sm">In the modern world of software development, ensuring reliability and performance is of paramount importance. However, despite the best efforts from the developers, software defects can still emerge, causing frustration and wasted resources. Due to the numerous defects found during the software development process, researchers have developed numerous ways for defect prediction models. However, these models cut down the time and expense of development when problems in a concurrent software product are anticipated. Due to the increased amount of defects brought on by software complexity, manual defect detection can become an extremely time-consuming procedure. This encouraged researchers to create methods for the automatic detection of software defects. The study of this paper has shown that a combination of machine learning algorithms could be applied effectively for software defect prediction. Interestingly, the combination of Artificial Neural Network and Random Forest classifier has been performed with the mean accuracy of 91%, while the hyper-parameter optimization model classifier has been performed with the mean accuracy of 83%, 83%, 84%, 77% and 80% for Support Vector Machine, Random Forest, Logistic Regression, Naive Bayes Gaussian and Decision Tree, respectively. These findings have demonstrated the potential of Machine Learning in the area of software development.</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 Systematic Approach for Enhancing Software Defect Prediction Using Machine Learning","attachmentId":105819656,"attachmentType":"pdf","work_url":"https://www.academia.edu/106747693/A_Systematic_Approach_for_Enhancing_Software_Defect_Prediction_Using_Machine_Learning","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/106747693/A_Systematic_Approach_for_Enhancing_Software_Defect_Prediction_Using_Machine_Learning"><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":117075390,"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":117075390,"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_117075390" 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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data-collection-position="3" data-entity-id="24155230" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/24155230/DEFECT_EFFORT_PREDICTION_MODELS_IN_SOFTWARE_INTERNATIONAL_JOURNAL_OF_MANAGEMENT_IJM_IJM_I_A_E_M_E">DEFECT / EFFORT PREDICTION MODELS IN SOFTWARE INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) IJM © I A E M E</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3194628" href="https://iaeme.academia.edu/iaeme">iaeme iaeme</a></div><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":"DEFECT / EFFORT PREDICTION MODELS IN SOFTWARE INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) IJM © I A E M 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href="https://independent.academia.edu/JournalIJECE">International Journal of Electrical and Computer Engineering (IJECE)</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Electrical and Computer Engineering (IJECE), 2024</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":"Insights of effectivity analysis of learning-based approaches towards software defect prediction","attachmentId":112788497,"attachmentType":"pdf","work_url":"https://www.academia.edu/116744257/Insights_of_effectivity_analysis_of_learning_based_approaches_towards_software_defect_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 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