Dynamic Prioritization of Test Cases for Regression Testing using Machine Learning

Dynamic Prioritization of Test Cases for Regression Testing using Machine Learning | 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 Dynamic Prioritization of Test Cases for Regression Testing using Machine Learning Gajender Rao, Deepak Nandal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5986757/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Feb, 2026 Read the published version in International Journal of System Assurance Engineering and Management → Version 1 posted 5 You are reading this latest preprint version Abstract Test cases can be selected on the basis of various parameters i.e. importance, complexity, and prospective effect over the applications. Using these parameters, priority is assigned to each test case and these are executed in a particular sequence accordingly, to reveal the bugs in code. Researches have proposed various methods to achieve this goal this paper, presents a dynamic prioritization of test cases for regression testing using machine learning and its performance analysis using different algorithms and classifiers shows that its APFD (Average Perdition of Fault detection) is 0.589895, execution time is 2.410775, precision & recall is 1 (highest), f1-score is 0.97 (max) as compared to existing schemes. Test case prioritization Machine learning Regression testing Test case selection Full Text Cite Share Download PDF Status: Published Journal Publication published 16 Feb, 2026 Read the published version in International Journal of System Assurance Engineering and Management → Version 1 posted Reviewers agreed at journal 07 May, 2025 Reviewers invited by journal 07 May, 2025 Editor invited by journal 19 Mar, 2025 Editor assigned by journal 09 Feb, 2025 First submitted to journal 07 Feb, 2025 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5986757","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453355051,"identity":"9028e50c-08b0-4a6d-8373-781a8ad7957b","order_by":0,"name":"Gajender Rao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYJCCA5UNEnL8DIcPANkSMsRpOdtgYSzZeCwBpIWHOGvONlQkbjh8xgDEJqxF3r334IGDOyQSNxw78/nVjRoLHgb2w0c34NNieOZcwoGDZySMZ545u8065xjQYTxpaTfwapmRY3D4Y5uEbN+Ns9uMc9iAWiR4zAhqOXCwTYKx4f6bZ8Y5/4jQIi8B0aI44cAZ5se5bURoMeA5YwD2i2TDMTPm3D4JHjZCfpFv7zH+cHBHHSgqH3/O+QZksB8+ht+WAwg2mwSYxKccbEsDgs38gZDqUTAKRsEoGJkAAKVzVSS1tAU/AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0004-8148-2087","institution":"Guru Jambheshwar University of Science \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"Gajender","middleName":"","lastName":"Rao","suffix":""},{"id":453355052,"identity":"aff61deb-f208-4b40-aa6e-edbc18567866","order_by":1,"name":"Deepak Nandal","email":"","orcid":"","institution":"Guru Jambheshwar University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Nandal","suffix":""}],"badges":[],"createdAt":"2025-02-08 09:29:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5986757/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5986757/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13198-026-03144-9","type":"published","date":"2026-02-16T15:58:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103251222,"identity":"bcb04ca0-0c71-43bd-8e43-42a0b8046ca0","added_by":"auto","created_at":"2026-02-23 16:06:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":499390,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5986757/v1_covered_4136526a-cb15-400a-9aa4-172e595352ad.pdf"}],"financialInterests":"","formattedTitle":"Dynamic Prioritization of Test Cases for Regression Testing using Machine Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Test case prioritization, Machine learning, Regression testing, Test case selection","lastPublishedDoi":"10.21203/rs.3.rs-5986757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5986757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTest cases can be selected on the basis of various parameters i.e. importance, complexity, and prospective effect over the applications. Using these parameters, priority is assigned to each test case and these are executed in a particular sequence accordingly, to reveal the bugs in code. Researches have proposed various methods to achieve this goal this paper, presents a dynamic prioritization of test cases for regression testing using machine learning and its performance analysis using different algorithms and classifiers shows that its APFD (Average Perdition of Fault detection) is 0.589895, execution time is 2.410775, precision \u0026amp; recall is 1 (highest), f1-score is 0.97 (max) as compared to existing schemes.\u003c/p\u003e","manuscriptTitle":"Dynamic Prioritization of Test Cases for Regression Testing using Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 03:16:57","doi":"10.21203/rs.3.rs-5986757/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-05-07T17:08:40+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-07T15:24:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"International Journal of System Assurance Engineering and Management","date":"2025-03-19T17:57:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-10T03:08:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of System Assurance Engineering and Management","date":"2025-02-08T04:29:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"193994f1-ad69-49bf-86b9-250d4d826542","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:03:18+00:00","versionOfRecord":{"articleIdentity":"rs-5986757","link":"https://doi.org/10.1007/s13198-026-03144-9","journal":{"identity":"international-journal-of-system-assurance-engineering-and-management","isVorOnly":false,"title":"International Journal of System Assurance Engineering and Management"},"publishedOn":"2026-02-16 15:58:33","publishedOnDateReadable":"February 16th, 2026"},"versionCreatedAt":"2025-05-12 03:16:57","video":"","vorDoi":"10.1007/s13198-026-03144-9","vorDoiUrl":"https://doi.org/10.1007/s13198-026-03144-9","workflowStages":[]},"version":"v1","identity":"rs-5986757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5986757","identity":"rs-5986757","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}