{"paper_id":"28fa2278-a858-450f-bffe-f5b04f5303bc","body_text":"An Empirical Study of the Comparison of Task Recommendation Techniques and Similar Source Code in Open Source Software Projects | 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 of the Comparison of Task Recommendation Techniques and Similar Source Code in Open Source Software Projects Getúlio Coimbra Regis, Igor Wiese, Ivanilton Polato, Marco Aurélio Graciotto Silva, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6322361/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 Context: Managing issues in open-source software projects is challenging and costly, as many developers are casual and/or newcomers. On the one hand, maintainers must ensure the quality of issue descriptions and their labels and create mechanisms for recommending and assigning issues. On the other hand, to complete the issue, contributors must understand it and locate the artifacts related to a given functionality or the defect to be fixed. Objectives: This work aimed to conduct a comparative study of different models for recommending similar issues that could help developers with their contributions. Methods: We collected data on issues and pull requests from 35 open-source projects hosted on GitHub. We used the Term Frequency Inverse Document Frequency (TF-IDF), Sentence BERT (SBERT), and Word2Vec techniques to recommend similar issues and source code to assist newcomers' contributions. Results: The models based on the SBERT and TF-IDF techniques yielded better results in the recommendations generated than Word2Vec in the two evaluated scenarios (general issues and those marked as good for newcomers). SBERT was able to recommend past issues where the code used in the solution was approximately 17% similar to the actual solution of the issue used as a query to evaluate the models, reaching results similar to those of GPT 3.5 and GPT 4. Conclusion: Based on the empirical results obtained, we hope to take the next steps in transferring the knowledge gained to software projects and developers, especially by supporting newcomers developers during their first contribution. GitHub Good First Issue Issue Recommender Open Source Software 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-6322361\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":441402748,\"identity\":\"34e960aa-709a-47eb-b051-9e0d2f9e9e23\",\"order_by\":0,\"name\":\"Getúlio Coimbra 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