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

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 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 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Defect Severity Prediction Model Umamaheswara Sharma Bhutamapuram, Ravichandra Sadam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3801598/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In a critical software system, the testers have to spend an enormous amount of time and effort maintaining the software due to the continuous occurrence of defects. To reduce the time and effort of a tester, prior works in the literature are limited to using documented defect reports to automatically predict the severity of the defective software modules. In contrast, in this work, we propose a metric-based software defect severity prediction (SDSP) model that is built using a decision-tree incorporated self-training semi-supervised learning approach to classify the severity of the defective software modules. Empirical analysis of the proposed model on the AEEEM datasets suggests using the proposed approach as it successfully assigns suitable severity class labels to the unlabelled modules. On the other hand, numerous research studies have addressed the methodological aspects of SDSP models, but the gap in estimating the performance of a developed prediction using suitable measures remains unattempt. For this, we propose the risk factor, percent of the saved budget, loss in the saved budget, percent of remaining edits, percent of remaining edits, remaining service time, and gratuitous service time, to interpret the predictions in terms of project objectives. Empirical analysis of the proposed approach shows the benefit of using the proposed measures in addition to the traditional measures. Software Defect Severity Prediction Software Quality Performance Evaluation Measures Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3801598","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264167447,"identity":"c8147008-035d-4702-b0cc-ffa24dce6779","order_by":0,"name":"Umamaheswara Sharma Bhutamapuram","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYLACxgZmORAtAeYxQ0XZCGgxBqkAajEAamEmTktiA1wL3BocwODa4QfMhTus0+fP7zG88XHHHwZzdv5jEgw1dgx80g3YtdxOM2CeeSY9d8MxHmPLmWcMGCybmdkkGI4lM7DJHMChJcGAmbftcO4GNh4zad42AwaDw8xsNxjYDjCwSSTg0JL+AaQlXb4NqOUvXMs/fFpywLYkMBwDamGEaWFsw61F8nZOweGZbemGG46lFVv2thnzALWY/0jsS+bBpYXvdvrGx4Vt1vLyzYc33vjZJidncP7gY4MP3+zk5Gdg1wICh5E5PGAyAcbAAQhE3SgYBaNgFIx4AAB9rFLGfed5KwAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute of Technology Warangal","correspondingAuthor":true,"prefix":"","firstName":"Umamaheswara","middleName":"Sharma","lastName":"Bhutamapuram","suffix":""},{"id":264167450,"identity":"05554a69-93b3-480c-bd48-870bc4dd0f16","order_by":1,"name":"Ravichandra Sadam","email":"","orcid":"","institution":"National Institute of Technology Warangal","correspondingAuthor":false,"prefix":"","firstName":"Ravichandra","middleName":"","lastName":"Sadam","suffix":""}],"badges":[],"createdAt":"2023-12-24 18:44:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3801598/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3801598/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52836495,"identity":"2856e368-6ac9-49e7-9928-db4abd87fb7c","added_by":"auto","created_at":"2024-03-17 11:09:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":633797,"visible":true,"origin":"","legend":"","description":"","filename":"SQJDSTDSP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3801598/v1_covered_1db5ef2b-a17f-48f1-b141-d1752125d898.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"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","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Software Defect Severity Prediction, Software Quality, Performance Evaluation Measures, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-3801598/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3801598/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In a critical software system, the testers have to spend an enormous amount of time and effort maintaining the software due to the continuous occurrence of defects. 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