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To help in this, numerous software metrics and statistical models have been developed, with a correspondingly large literature. We provide a critical review of this literature and the state-of-the-art. Most of the wide range of prediction models use size and complexity metrics to predict defects. Others are based on testing data, the \"quality\" of the development process, or take a multivariate approach. The authors of the models have often made heroic contributions to a subject otherwise bereft of empirical studies. However, there are a number of serious theoretical and practical problems in many studies. The models are weak because of their inability to cope with the, as yet, unknown relationship between defects and failures. There are fundamental statistical and data quality problems that undermine model validity. More significantly many prediction models tend to model only part of the underlying problem and seriously misspecify it. To illustrate these points the \"Goldilock's Conjecture,\" that there is an optimum module size, is used to show the considerable problems inherent in current defect prediction approaches. Careful and considered analysis of past and new results shows that the conjecture lacks support and that some models are misleading. We recommend holistic models for software defect prediction, using Bayesian Belief Networks, as alternative approaches to the single-issue models used at present. We also argue for research into a theory of \"software decomposition\" in order to test hypotheses about defect introduction and help construct a better science of software engineering.","publication_date":"1999,,","publication_name":"IEEE Transactions on Software Engineering","grobid_abstract_attachment_id":"35788349"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A Critique of Software Defect Prediction Models","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [22640469]; 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":35788349,"attachmentType":"pdf"}"><img alt="First page of “A Critique of Software Defect Prediction Models”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/35788349/mini_magick20190311-25796-1tkdo89.png?1552368850" /><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">A Critique of Software Defect Prediction Models</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="22640469" href="https://qmul.academia.edu/NormanFenton"><img alt="Profile image of Norman Fenton" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/22640469/26246644/24842955/s65_norman.fenton.jpg" />Norman Fenton</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">1999, IEEE Transactions on Software Engineering</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">15 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 9574037; 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class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/5107648/A_Study_of_Empirical_Prediction_of_Defect"><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="34314425" 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/34314425/Predicting_Software_Faults_Using_Software_Metrics_A_Review">Predicting Software Faults Using Software Metrics: A 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="59400872" href="https://amu-in.academia.edu/JunaidReshi">Junaid A Reshi</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents a survey of 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It studies various techniques, their advantages and limitations in predicting the software defects. Software metrics find essence in predicting software defects and thus enhancing the quality of a software, while keeping the costs and efforts to minimal. As there has been a gap between academy and industry in this field, a technique is examined for bridging the gap by studying practical implementation that are feasible for the industry. Various papers have been studied and various methods proposed have been analyzed for their possible shortcomings and enhancements. The results and the findings of various authors are channelized to examine the problems and their possible solutions. The approach of Bayesian Belief Nets is discussed at the end of the paper as a candidate for improved decision making in the industry whenever uncertainty reigns over decision making.</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 Faults Using Software Metrics: A Review","attachmentId":54215256,"attachmentType":"pdf","work_url":"https://www.academia.edu/34314425/Predicting_Software_Faults_Using_Software_Metrics_A_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/34314425/Predicting_Software_Faults_Using_Software_Metrics_A_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="7" data-entity-id="16827135" 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/16827135/Building_Defect_Prediction_Models_in_Practice">Building Defect Prediction Models in Practice</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="36583123" href="https://independent.academia.edu/JohannesHimmelbauer">Johannes Himmelbauer</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="36276263" href="https://independent.academia.edu/ThomasNatschl%C3%A4ger">Thomas Natschläger</a></div><p class="ds-related-work--metadata ds2-5-body-xs">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":"Building Defect Prediction Models in Practice","attachmentId":42391019,"attachmentType":"pdf","work_url":"https://www.academia.edu/16827135/Building_Defect_Prediction_Models_in_Practice","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/16827135/Building_Defect_Prediction_Models_in_Practice"><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="124273809" 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/124273809/The_Effect_of_the_Dataset_Size_on_the_Accuracy_of_Software_Defect_Prediction_Models_An_Empirical_Study">The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An 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="169048435" href="https://ua-huntsville.academia.edu/MohammadAlshayeb">Mohammad Alshayeb</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Inteligencia Artificial, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">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-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 Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study","attachmentId":118530691,"attachmentType":"pdf","work_url":"https://www.academia.edu/124273809/The_Effect_of_the_Dataset_Size_on_the_Accuracy_of_Software_Defect_Prediction_Models_An_Empirical_Study","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/124273809/The_Effect_of_the_Dataset_Size_on_the_Accuracy_of_Software_Defect_Prediction_Models_An_Empirical_Study"><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":35788349,"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":35788349,"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_35788349" 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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ds2-5-body-xs">Proceedings of the 6th International Conference on Predictive Models in Software Engineering - PROMISE '10, 2010</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":"Usage of multiple prediction models based on defect categories","attachmentId":81696880,"attachmentType":"pdf","work_url":"https://www.academia.edu/73000052/Usage_of_multiple_prediction_models_based_on_defect_categories","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-related-work-grid-card-view-pdf" href="https://www.academia.edu/73000052/Usage_of_multiple_prediction_models_based_on_defect_categories"><span class="ds2-5-text-link__content">View PDF</span><span 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Gao</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Transactions on Reliability, 2007</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 Comprehensive Empirical Study of Count Models for Software Fault Prediction","attachmentId":97932902,"attachmentType":"pdf","work_url":"https://www.academia.edu/95873846/A_Comprehensive_Empirical_Study_of_Count_Models_for_Software_Fault_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-related-work-grid-card-view-pdf" href="https://www.academia.edu/95873846/A_Comprehensive_Empirical_Study_of_Count_Models_for_Software_Fault_Prediction"><span class="ds2-5-text-link__content">View 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