Understanding Open Source Contributor Profiles in Popular Machine Learning Libraries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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’ 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: Core-Nighttime , Core-Daytime , Peripheral-Nighttime , and Peripheral-Daytime . 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; and (4) long-term contributors evolve toward making fewer, constant, balanced and less technical contributions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it