Azala

Understanding Open-Source Contributor Profiles in Popular Machine Learning Libraries

Gorde:
Argitaratua izan da:ProQuest Dissertations and Theses (2025)
Egile nagusia: Liu, Jiawen
Argitaratua:
ProQuest Dissertations & Theses
Gaiak:
Software development
Deep learning
Open source software
Community
Clustering
Workloads
Mann-Whitney U test
Software quality
Machine learning
Programming languages
Working hours
Public domain
Systems development
Computer engineering
Libraries
Popularity
Software engineering
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Laburpena:With the increasing popularity of machine learning (ML), many open-source software (OSS) contributors are attracted to developing and adopting ML approaches. A 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 thesis, we aim to understand ML contributors by identifying contributor profiles in ML libraries. We further study contributors’ OSS engagement from four aspects: workload composition, work preferences, technical importance, and ML-specific contributions. By investigating 11,949 contributors from 8 popular ML libraries (TensorFlow, PyTorch, scikit-learn, Keras, MXNet, Theano/Aesara, ONNX, and deeplearning4j), we identify four contributor profiles: Core-Afterhour, Core-Workhour, Peripheral-Afterhour, and Peripheral-Workhour. 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’ work preferences and workload compositions are significantly correlated with project popularity; 4) long-term contributors evolve towards making fewer, constant, balanced and less technical contributions.
ISBN:9798311909303
Baliabidea:ProQuest Dissertations & Theses Global