An Empirical Study towards dealing with Noise and Class Imbalance issues in Software Defect Prediction

An Empirical Study towards dealing with Noise and Class Imbalance issues in Software Defect Prediction | 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 An Empirical Study towards dealing with Noise and Class Imbalance issues in Software Defect Prediction SUSHANT KUMAR PANDEY, Anil Kumar Tripathi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-549406/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2021 Read the published version in Soft Computing → Version 1 posted 5 You are reading this latest preprint version Abstract The quality of the defect datasets is a critical issue in the domain of software defect prediction (SDP). These datasets are obtained through the mining of software repositories. Resent studies claims over the quality of the defect dataset. It is because of inconsistency between bug/clean fix keyword in fault reports and the corresponding link in the change management logs. Class Imbalance (CI) problem is also a big challenging issue in SDP models. The defect prediction method trained using noisy and imbalanced data leads to inconsistent and unsatisfactory results. Combined analysis over noisy instances and CI problem needs to be required. To the best of our knowledge, there are insufficient studies that have been done over such aspects. In this paper, we deal with the impact of noise and CI problem on five baseline SDP models; we manually added the various noise level (0 to 80%) and identified its impact on the performance of those SDP models. Moreover, we further provide guidelines for the possible range of tolerable noise for baseline models. We have also suggested the SDP model, which has the highest noise tolerable ability and outperforms over other classical methods. The True Positive Rate (TPR) and False Positive Rate (FPR) values of the baseline models reduce between 20% to 30% after adding 10% to 40% noisy instances. Similarly, the ROC (Receiver Operating Characteristics) values of SDP models reduces to 40% to 50%. The suggested model leads to avoid noise between 40% to 60% as compared to other traditional models. Theoretical Computer Science Software testing Software fault prediction Class imbalance Noisy instance Machine learning Software metrics Fault proneness. Full Text Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2021 Read the published version in Soft Computing → Version 1 posted Editorial decision: Accept 29 Jul, 2021 Reviews received at journal 14 Jul, 2021 Reviewers invited by journal 14 Jul, 2021 Editor assigned by journal 24 May, 2021 First submitted to journal 21 May, 2021 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-549406","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":39552796,"identity":"6098bea8-d98a-496d-9ab0-eaa6c5cb70d8","order_by":0,"name":"SUSHANT KUMAR PANDEY","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-1882-2435","institution":"Indian Institute of Technology Banaras Hindu University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"SUSHANT","middleName":"KUMAR","lastName":"PANDEY","suffix":""},{"id":39552797,"identity":"6d2e2a4e-4ed1-4166-914e-f438eda97e55","order_by":1,"name":"Anil Kumar Tripathi","email":"","orcid":"","institution":"Indian Institute of Technology BHU Varanasi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anil","middleName":"Kumar","lastName":"Tripathi","suffix":""}],"badges":[],"createdAt":"2021-05-21 19:28:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-549406/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-549406/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00500-021-06096-3","type":"published","date":"2021-08-13T15:00:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":13660119,"identity":"22943b82-9866-48ed-9515-7027be01639d","added_by":"auto","created_at":"2021-09-17 10:22:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1104776,"visible":true,"origin":"","legend":"","description":"","filename":"noisedealing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-549406/v1_covered.pdf"},{"id":11607539,"identity":"351529bd-a8a9-4caf-a888-544b79cf0564","added_by":"auto","created_at":"2021-07-19 19:55:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1096296,"visible":true,"origin":"","legend":"","description":"","filename":"noisedealing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-549406/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"An Empirical Study towards dealing with Noise and Class Imbalance issues in Software Defect Prediction","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-549406/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Software testing, Software fault prediction, Class imbalance, Noisy instance, Machine learning, Software metrics, Fault proneness.","lastPublishedDoi":"10.21203/rs.3.rs-549406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-549406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The quality of the defect datasets is a critical issue in the domain of software defect prediction (SDP). 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We have also suggested the SDP model, which has the highest noise tolerable ability and outperforms over other classical methods. The True Positive Rate (TPR) and False Positive Rate (FPR) values of the baseline models reduce between 20\\% to 30\\% after adding 10% to 40% noisy instances. Similarly, the ROC (Receiver Operating Characteristics) values of SDP models reduces to 40% to 50%. 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