Improving Ensemble Models for Software Defect Prediction: a study applying preprocessing techniques

Improving Ensemble Models for Software Defect Prediction: a study applying preprocessing techniques | 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 Improving Ensemble Models for Software Defect Prediction: a study applying preprocessing techniques Bianca P. R. Vieira, Rogério E. Garcia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7483430/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 Defect prediction in software is a practice to improve the quality of software. However, the methods proposed to detect defects efficiently have challenges. Methods based on mining software repositories face challenges like the high dimensionality of data sets and the imbalance of datasets from software repositories. The need to deal with imbalanced data scenarios and large feature sets motivates the search to improve defect prediction models' effectiveness. Related works have studied ensemble models, feature selection, and imbalanced data, but have not analyzed their individual and combined impact with real-world datasets. The general purpose is to enhance the mining of software repositories to detect defects. We collected data from three open-source repositories, preprocessed using feature selection and data balance techniques, and developed models to compare with the same model algorithms but without preprocessing. The results are promising, showing improvement on final general metrics, as well as the metrics for the minority class. All the code developed in this research is available in the GitHub repository SoftDefectProcess Mining Software Repository Preprocessing Feature Selection Data Balance 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-7483430","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517274187,"identity":"6c3e0b0e-a88c-487c-b48a-13fc518745c0","order_by":0,"name":"Bianca P. R. 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