{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:41:55Z","timestamp":1760060515628,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T00:00:00Z","timestamp":1757116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hubei Provincial Department of Education Philosophy and Social Sciences Research Project: Youth Project","award":["24Q166"],"award-info":[{"award-number":["24Q166"]}]},{"name":"Scientific Research Startup Foundation Project of Wuhan Vocational College of Software and Engineering","award":["24Q166"],"award-info":[{"award-number":["24Q166"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>GitHub hosts over 10 million repositories, providing researchers with vast opportunities to study diverse software engineering problems. However, as anyone can create a repository for any purpose at no cost, open-source platforms contain many non-cooperative or non-developmental noise projects (e.g., repositories of dotfiles). When selecting open-source projects for analysis, mixing collaborative coding projects (e.g., machine learning frameworks) with noisy projects may bias research findings. To solve this problem, we optimize the Semi-Automatic Decision Tree Method (SADTM), an existing Collaborative Coding Project (CCP) classification method, to improve its generality and accuracy. We evaluate our method on the GHTorrent dataset (2012\u20132020) and find that it effectively enhances CCP classification in two key ways: (1) it demonstrates greater stability than existing methods, yielding consistent results across different datasets; (2) it achieves high precision, with an F-measure ranging from 0.780 to 0.893. Our method outperforms existing techniques in filtering noise and selecting CCPs, enabling researchers to extract high-quality open-source projects from candidate samples with reliable accuracy.<\/jats:p>","DOI":"10.3390\/info16090774","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T09:32:01Z","timestamp":1757496721000},"page":"774","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Generalized Method for Filtering Noise in Open-Source Project Selection"],"prefix":"10.3390","volume":"16","author":[{"given":"Yi","family":"Ding","sequence":"first","affiliation":[{"name":"Information College, Wuhan Vocational College of Software and Engineering, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Fang","sequence":"additional","affiliation":[{"name":"Normal College, Jingchu University of Technology, Jingmen 448000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1768-9014","authenticated-orcid":false,"given":"Xiaoyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Information College, Wuhan Vocational College of Software and Engineering, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gousios, G., and Spinellis, D. 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