[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125157-en":3,"doc-seo-125157-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125157,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Understanding Open Source Contributor Profiles in Popular Machine Learning Libraries","Machine learning’s rapid adoption has drawn many developers to open-source software projects, making contributor understanding essential for effective development and maintenance. Existing work largely relies on user surveys, leaving gaps in activity-based profiling from software repositories. This paper identifies contributor profiles in major ML libraries by analyzing 7,640 contributors across workload composition, work preferences, and technical importance, and reports how profiles relate to collaboration, project popularity, and long-term contribution evolution.","arXiv :2406 .05685v 1 [ cs . SE] 9 Jun 2024  \nUnderstanding Open Source Contributor Profiles in Popular Machine Learning Libraries  \nJIAWEN LIU, Department of Electrical and Computer Engineering, Queen’s University, Canada HAOXIANG ZHANG, Software Analysis and Intelligence Lab (SAIL), Queen’s University, Canada YING ZOU, Department of Electrical and Computer Engineering, Queen’s University, Canada  \nWith 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 by user surveys. There is a lack of understanding of ML contributors based on their activities tracked from software repositories. In this paper, we aim to understand ML contributors by identifying contributor profiles in ML libraries. We further study contributors’ OSS engagement from three aspects: workload composition, work preferences, and technical importance. By investigating 7,640 contributors from 6 popular ML libraries (TensorFlow, PyTorch, Keras, MXNet, Theano, and ONNX), we identify four contributor profiles: Core-Afterhour, Core-Workhour, Peripheral-Afterhour, and Peripheral-Workhour. We find that: 1) project experience, authored files, collaborations, and geological location are significant features of all profiles; 2) contributors in Core profiles exhibit significantly different OSS engagement compared to Peripheral profiles; 3) contributors’work preferences and workload compositions significantly impact project popularity;  \n4) long-term contributors evolve towards making fewer, constant, balanced and less technical contributions.  \nCCS Concepts: • Software and its engineering → Programming teams.  \nAdditional Key Words and Phrases: Open Source Software, Developer Profiles, Collaborative Software Development, Deep Learning Libraries  \nACM Reference Format:  \nJiawen Liu, Haoxiang Zhang, and Ying Zou. 2024. Understanding Open Source Contributor Profiles in Popular Machine Learning Libraries. 1, 1 (June 2024), 44 pages. [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \n1 Introduction  \nOpen Source Software (OSS) has emerged as a dominant model in software development, gaining widespread recognition among enterprises and developers as the preferred approach for software development. The OSS community comprises globally distributed contributors and users with shared interests, who actively participate in knowledge sharing and collaborate on the development and maintenance of software projects. Anyone with the necessary knowledge and skills can be a contributor to OSS projects. They can contribute in various ways, such as writing source code, updating documentation, reporting issues, conducting code reviews, and participating in discussions. These activities are critical  \nAuthors’ addresses: Jiawen Liu, Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada, [jiawen.liu@queensu.ca](jiawen.liu@queensu.ca); Haoxiang Zhang, Software Analysis and Intelligence Lab (SAIL), Queen’s University, Kingston, Canada; Ying Zou, Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or toredistribute to list","cbCaiubAVXzR2Ccr","https://ap.wps.com/l/cbCaiubAVXzR2Ccr","pdf",6000809,1,44,"English","en",105,"# Introduction\n## Research Motivation and Gap\n## Contributor Profiling Approach\n## Dataset and Contributor Profile Types\n## Key Findings\n# Method and Analysis (Inferred)\n## Workload Composition\n## Work Preferences\n## Technical Importance\n## Long-term Evolution","[{\"question\":\"What problem does the paper address about open-source ML contributors?\",\"answer\":\"It targets the lack of activity-based understanding of ML contributors from software repositories, which limits efficient resource allocation and collaboration in OSS projects.\"},{\"question\":\"How does the paper study contributor engagement in ML libraries?\",\"answer\":\"It analyzes contributor activity across workload composition, work preferences, and technical importance using 7,640 contributors from six popular ML libraries.\"},{\"question\":\"What contributor profiles are identified, and what key differences are observed?\",\"answer\":\"The study identifies four profiles—Core-Afterhour, Core-Workhour, Peripheral-Afterhour, and Peripheral-Workhour—and shows Core profiles have significantly different OSS engagement than Peripheral profiles, influenced by preferences and workload composition.\"}]","Understanding Open Source Contributor Profiles in Popular Machine Learning Libraries | 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problem does the paper address about open-source ML contributors?","Question",{"text":75,"@type":76},"It targets the lack of activity-based understanding of ML contributors from software repositories, which limits efficient resource allocation and collaboration in OSS projects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper study contributor engagement in ML libraries?",{"text":80,"@type":76},"It analyzes contributor activity across workload composition, work preferences, and technical importance using 7,640 contributors from six popular ML libraries.",{"name":82,"@type":73,"acceptedAnswer":83},"What contributor profiles are identified, and what key differences are observed?",{"text":84,"@type":76},"The study identifies four profiles—Core-Afterhour, Core-Workhour, Peripheral-Afterhour, and Peripheral-Workhour—and shows Core profiles have significantly different OSS engagement than Peripheral profiles, influenced by preferences and workload 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