A Novel Approach to Enhance Software Defect Prediction using An Improved Grey Wolf Optimization based Extreme Learning Machine Technique

A Novel Approach to Enhance Software Defect Prediction using An Improved Grey Wolf Optimization based Extreme Learning Machine Technique | 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 Article A Novel Approach to Enhance Software Defect Prediction using An Improved Grey Wolf Optimization based Extreme Learning Machine Technique Saurav Mallik, Debasish Pradhan, Debendra Muduli, Adyasha Rath, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4110665/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 software development and testing, detecting and mitigating faults are paramount to prevent potential issues from escalating and disrupting the development and testing processes. The proposed method can also improve the prediction of various is sues, such as increased model complexity, longer execution times, higher error rates, and enhanced fault detection capabilities. Addressing this concern, the paper introduced a three-stage model encompassing data pre-processing, feature dimensionality reduction, and fault prediction, which are essential steps in effective software testing. Our research leverages the publicly available NASA dataset and employs Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to re duce feature vector dimensions, a common practice in software testing. We propose an improved version of the Grey Wolf Optimization (IMGWO) algorithm, complemented by Extreme Learning Machines (ELM), to discern the presence of defects within software modules. This approach is highly relevant in software testing, as it aids in identifying problematic areas early in the development cycle. Utilizing the PCA-LDA+IMGWO-ELM approach, our model achieves an average accuracy rate of 0.9811 when applied to the KC2 dataset, a significant milestone in software testing. These results are substantiated through experimental validation, reinforcing the credibility of our approach in predicting potential software defects during the software testing phase. Physical sciences/Engineering Physical sciences/Mathematics and computing 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. 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-4110665","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":285852624,"identity":"71bebd6a-9d72-405f-a7aa-9fb237cd6c38","order_by":0,"name":"Saurav Mallik","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHADHsYHH4AUGzuR6iWAWpgNZ4C0MJOghU2aB8QkpEW+/YzZh59t2+r42c8ekLb5tU2ej5mB8cPHHNxaDM7kGM/sbbstIdmTl2Cc23fbsI2ZgVly5jY8WhhyjBl4ztyWMLjBY5Cc23ObEaiFjZkXjxb5/jfGjH+AWuyBWg5b9ty2J6iF4UaOMTNPBdAWCR7DZoYftxMJajG48ayYWabituQMoKcYextuJ7cxMzbj9Yt8f/JmxjcGt/n528+Y//jx57bt/Pbmgx8+4nMYCmBsA5MNxKoHgT+kKB4Fo2AUjIKRAgBPfUxjU7ne/wAAAABJRU5ErkJggg==","orcid":"","institution":"Harvard T H Chan School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Saurav","middleName":"","lastName":"Mallik","suffix":""},{"id":285852625,"identity":"fccb6d76-ddb8-48b4-9b7e-ce9a1c9fcb3e","order_by":1,"name":"Debasish Pradhan","email":"","orcid":"","institution":"C.V. Raman Global University","correspondingAuthor":false,"prefix":"","firstName":"Debasish","middleName":"","lastName":"Pradhan","suffix":""},{"id":285852626,"identity":"418a5abd-a705-4b20-83b3-47512f7c8777","order_by":2,"name":"Debendra Muduli","email":"","orcid":"","institution":"C.V. Raman Global University","correspondingAuthor":false,"prefix":"","firstName":"Debendra","middleName":"","lastName":"Muduli","suffix":""},{"id":285852627,"identity":"a4c6be09-404e-4a62-9cd5-a5629aa30a85","order_by":3,"name":"Adyasha Rath","email":"","orcid":"","institution":"C.V. Raman Global University","correspondingAuthor":false,"prefix":"","firstName":"Adyasha","middleName":"","lastName":"Rath","suffix":""},{"id":285852628,"identity":"d79f4973-4869-4e03-89a3-1691dd1490cc","order_by":4,"name":"Ganapati Panda","email":"","orcid":"","institution":"C.V. Raman Global University","correspondingAuthor":false,"prefix":"","firstName":"Ganapati","middleName":"","lastName":"Panda","suffix":""},{"id":285852629,"identity":"3b40b4a3-41f5-44fe-92d9-d22e63c37e1f","order_by":5,"name":"Sujata Dash","email":"","orcid":"","institution":"Nagaland University","correspondingAuthor":false,"prefix":"","firstName":"Sujata","middleName":"","lastName":"Dash","suffix":""},{"id":285852630,"identity":"4f86c63a-9def-49ad-8c26-93c7d7d1af6a","order_by":6,"name":"Hong Qin","email":"","orcid":"","institution":"The University of Tennessee at Chattanooga","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2024-03-16 00:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4110665/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4110665/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78479893,"identity":"3e000d35-1127-476e-9a8c-b870ad83770b","added_by":"auto","created_at":"2025-03-13 18:31:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1195478,"visible":true,"origin":"","legend":"","description":"","filename":"ANovelApproachtoEnhanceSoftwareDefectPredictionusingAnImprovedGreyWolfOptimizationbasedExtremeLearningMachineTechnique3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4110665/v1_covered_ca8e396b-ebf4-40cf-ae77-0938e70978e0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Approach to Enhance Software Defect Prediction using An\r\n Improved Grey Wolf Optimization based Extreme Learning\r\n Machine Technique","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4110665/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4110665/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In software development and testing, detecting and mitigating faults are paramount to prevent potential issues from escalating\r\n and disrupting the development and testing processes. The proposed method can also improve the prediction of various is\r\nsues, such as increased model complexity, longer execution times, higher error rates, and enhanced fault detection capabilities.\r\n Addressing this concern, the paper introduced a three-stage model encompassing data pre-processing, feature dimensionality\r\n reduction, and fault prediction, which are essential steps in effective software testing. Our research leverages the publicly\r\n available NASA dataset and employs Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to re\r\nduce feature vector dimensions, a common practice in software testing. We propose an improved version of the Grey Wolf\r\n Optimization (IMGWO) algorithm, complemented by Extreme Learning Machines (ELM), to discern the presence of defects\r\n within software modules. This approach is highly relevant in software testing, as it aids in identifying problematic areas early\r\n in the development cycle. Utilizing the PCA-LDA+IMGWO-ELM approach, our model achieves an average accuracy rate of\r\n 0.9811 when applied to the KC2 dataset, a significant milestone in software testing. These results are substantiated through\r\n experimental validation, reinforcing the credibility of our approach in predicting potential software defects during the software testing phase.","manuscriptTitle":"A Novel Approach to Enhance Software Defect Prediction using An\n Improved Grey Wolf Optimization based Extreme Learning\n Machine Technique","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 06:18:35","doi":"10.21203/rs.3.rs-4110665/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"6dacc742-c00a-43a0-9a8d-9c5cbe6607ad","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30088460,"name":"Physical sciences/Engineering"},{"id":30088461,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-03-13T18:23:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 06:18:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4110665","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4110665","identity":"rs-4110665","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}