{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T04:25:25Z","timestamp":1772943925444,"version":"3.50.1"},"reference-count":109,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    With the increasing popularity of machine learning (ML), many open source software (OSS) contributors are attracted to developing and adopting ML approaches. Comprehensive understanding of ML contributors is crucial for successful ML OSS development and maintenance. Without such knowledge, there is a risk of inefficient resource allocation and hindered collaboration in ML OSS projects. Existing research focuses on understanding the difficulties and challenges perceived by ML contributors through user surveys. There is a lack of understanding of ML contributors based on their activities recorded in the software repositories. In this article, we aim to understand ML contributors by identifying contributor profiles in ML libraries. We further study contributors\u2019 OSS engagement from four aspects: workload composition, work preferences, technical importance, and ML-specific versus SE contributions. By investigating 11,949 contributors from eight popular ML libraries (i.e., TensorFlow, PyTorch, scikit-learn, Keras, MXNet, Theano\/Aesara, ONNX, and deeplearning4j), we categorize them into four contributor profiles:\n                    <jats:italic toggle=\"yes\">Core-Nighttime<\/jats:italic>\n                    ,\n                    <jats:italic toggle=\"yes\">Core-Daytime<\/jats:italic>\n                    ,\n                    <jats:italic toggle=\"yes\">Peripheral-Nighttime<\/jats:italic>\n                    , and\n                    <jats:italic toggle=\"yes\">Peripheral-Daytime<\/jats:italic>\n                    . We find that: (1) project experience, authored files, collaborations, pull requests comments received and approval ratio, and geographical location are significant features of all profiles; (2) contributors in Core profiles exhibit significantly different OSS engagement compared to Peripheral profiles; (3) contributors\u2019 work preferences and workload compositions are significantly correlated with project popularity; and (4) long-term contributors evolve toward making fewer, constant, balanced and less technical contributions.\n                  <\/jats:p>","DOI":"10.1145\/3747347","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T10:00:42Z","timestamp":1751882442000},"page":"1-59","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Understanding Open Source Contributor Profiles in Popular Machine Learning Libraries"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5617-1487","authenticated-orcid":false,"given":"Jiawen","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Queen\u2019s University, Kingston, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3921-1724","authenticated-orcid":false,"given":"Haoxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Software Analysis and Intelligence Lab (SAIL), Queen\u2019s University, Kingston, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5335-0261","authenticated-orcid":false,"given":"Ying","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Queen\u2019s University, Kingston, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"GitHub. 2022. 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