EHHO-EL: Hybrid Method for Software defect Detection in Software Product Lines using Extended Harris Hawks Optimization and Ensemble Learning

EHHO-EL: Hybrid Method for Software defect Detection in Software Product Lines using Extended Harris Hawks Optimization and Ensemble 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 EHHO-EL: Hybrid Method for Software defect Detection in Software Product Lines using Extended Harris Hawks Optimization and Ensemble Learning Mehdi Habibzadeh khameneh, Akbar Nabiollahi-Najafabadi, Reza Tavoli, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5379879/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2025 Read the published version in The Journal of Supercomputing → Version 1 posted 3 You are reading this latest preprint version Abstract Software Product Line (SPL) aims to reduce development costs and time while improving quality, but the complexity and involvement of multiple design teams often lead to defects and delays. Detecting and resolving defects in large-scale industrial SPLs remains a significant research area. This study proposes a hybrid approach that combines the Harris Hawks Optimization (HHO) algorithm with stacking-based ensemble learning for defect detection in SPLs. Enhanced by the Chaos Optimization Algorithm (COA) to avoid local optima and improve accuracy, the approach is evaluated on two datasets, LVAT and NASA, This study incorporates four datasets from each of these repositories. The experiment results show that the proposed method achieves detection accuracy rates of 92.7%, 91.1%, 96.3%, 98.4% for the LTS1, LTM2, LTL3, LTV4 and 97.91%, 99.01%, 94.21%, 90.93% for the CM1, JM1, KC1, PC1. Statistical tests confirm that this method offers superior accuracy and faster convergence compared to existing methods. Software production lines Meta-heuristic algorithms Software defect Detection Harris Hawks Optimization algorithm Stacking-based ensemble learning Product No defect Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Mar, 2025 Read the published version in The Journal of Supercomputing → Version 1 posted Editor assigned by journal 03 Nov, 2024 Submission checks completed at journal 03 Nov, 2024 First submitted to journal 02 Nov, 2024 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. 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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-5379879","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373500169,"identity":"dd5a99d6-dc3d-4f10-811e-ad4b23dd67dd","order_by":0,"name":"Mehdi Habibzadeh khameneh","email":"","orcid":"","institution":"Islamic Azad University of Najafabad","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"Habibzadeh","lastName":"khameneh","suffix":""},{"id":373500170,"identity":"467ea42c-a301-4ab5-8673-451f11331a96","order_by":1,"name":"Akbar Nabiollahi-Najafabadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie2RsQrCMBCGr7hGuxaU+gopBRfFZ2kpOEpBEMeA0C66+xg+woUbXIquQp2dXMRJVDAtFFxMdRPMNx3h//gvCYDB8KM0gA/KAasTfJN8VUZfK0BfLGWLprzE8W5sp8LCa3IAO0WLZhrFwVbUXvF84mQIcpkcwckCkJmuBhlvM56HaycAbCZqwz2AFBqji8y/Mb4tFflQSrdO4ch6qgVLhYoWXqd4xHr9FY/UXaSgzpaYl4VCq7ibhZ/H96F6sTmdT1Ny3Q3RWacUn1IQAFhljlVDLcFHKYPBYPhPnnZtTtFBsDGIAAAAAElFTkSuQmCC","orcid":"","institution":"Islamic Azad University of Najafabad","correspondingAuthor":true,"prefix":"","firstName":"Akbar","middleName":"","lastName":"Nabiollahi-Najafabadi","suffix":""},{"id":373500171,"identity":"7ff9b205-c5bc-4a32-91e7-1decb9e710f8","order_by":2,"name":"Reza Tavoli","email":"","orcid":"","institution":"Islamic Azad University of Chalous","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Tavoli","suffix":""},{"id":373500172,"identity":"60513f09-4db4-4ff1-a3a8-f9e6f3281934","order_by":3,"name":"Hamid Rastegari","email":"","orcid":"","institution":"Islamic Azad University of Najafabad","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Rastegari","suffix":""}],"badges":[],"createdAt":"2024-11-02 21:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5379879/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5379879/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11227-025-06986-5","type":"published","date":"2025-03-03T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78190839,"identity":"26d07250-d96d-4da7-a9c3-c1fdcd6e026a","added_by":"auto","created_at":"2025-03-10 19:51:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":789829,"visible":true,"origin":"","legend":"","description":"","filename":"MehdiHabibzadehKhManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5379879/v1_covered_89f05f87-625a-4987-92e4-11446479b30f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"EHHO-EL: Hybrid Method for Software defect Detection in Software Product Lines using Extended Harris Hawks Optimization and Ensemble 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":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Software production lines, Meta-heuristic algorithms, Software defect Detection, Harris Hawks Optimization algorithm, Stacking-based ensemble learning, Product No defect","lastPublishedDoi":"10.21203/rs.3.rs-5379879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5379879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoftware Product Line (SPL) aims to reduce development costs and time while improving quality, but the complexity and involvement of multiple design teams often lead to defects and delays. 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