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Towards a change taxonomy for machine learning pipelines

Empirical study of ML pipelines and forks related to academic publications

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Abstract

Machine Learning (ML) academic publications commonly provide open-source implementations on GitHub, allowing their audience to replicate, validate, or even extend the ML algorithms, data sets and metadata. However, thus far little is known about the degree of collaboration activity happening on such ML research repositories, in particular regarding (1) the degree to which such repositories receive contributions from forks, (2) the nature of such contributions (i.e., the types of changes), and (3) the nature of changes that are not contributed back to forks, which might represent missed opportunities. In this paper, we empirically study contributions to 1,346 ML research repositories and their 67,369 forks, both quantitatively and qualitatively, by building on Hindle et al.’s seminal taxonomy of code changes. We found that while ML research repositories are heavily forked, only 9% of the forks made modifications to the forked repository. 42% of the latter sent changes to the parent repositories, half of which (52%) were accepted by the parent repositories. Our qualitative analysis on 539 contributed and 378 local (fork-only) changes extends Hindle et al.’s taxonomy with two new top-level change categories related to ML (Data and Dependency Management), and 16 new sub-categories, including nine ML-specific ones (input data, parameter tuning, pre-processing, training infrastructure, model structure, pipeline performance, sharing, validation infrastructure, and output data). While the changes that are not contributed back by the forks mostly concern domain-specific features and local experimentation (e.g., parameter tuning), the origin repositories do miss out on a non-trivial 15.4% of Documentation changes, 13.6% of Feature changes and 11.4% of Bug fix changes.

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Data Availability

All the scripts along with the mined data are provided in the replication packageFootnote 27

Notes

  1. https://www.tensorflow.org

  2. https://pytorch.org

  3. https://paperswithcode.com

  4. https://modeldepot.io/

  5. In August 2022, PapersWithCode indexed more than 77,640 academic AI publications along with their code bases.

  6. In the remainder of this paper, we use the term “ML research repositories” to identify such ML pipelines.

  7. https://twitter.com/paperswithcode/status/1091315540092768257

  8. https://web.archive.org/web/20190404211946/https://modeldepot.io/search/results?q=

  9. https://arxiv.org/abs/1512.03385

  10. https://github.com/thtrieu/darkflow

  11. https://arxiv.org/pdf/1506.02640.pdf

  12. https://arxiv.org/pdf/1612.08242.pdf

  13. https://github.com/mohammed-elkomy/two-stream-action-recognition

  14. https://arxiv.org/pdf/1406.2199.pdf

  15. https://arxiv.org/pdf/1604.07669.pdf

  16. https://arxiv.org/pdf/1507.02159.pdf

  17. https://docs.github.com/en/rest/reference/search

  18. PRs on GitHub are not limited to just one commit.

  19. https://github.com/BoseAslCohort/youtube-8m/commit/c1b01315bafc24e83248cd862a9324bb21d4d52d

  20. https://docs.github.com/en/rest/reference/repos

  21. https://github.com/SullyChen/Autopilot-TensorFlow

  22. https://arxiv.org/pdf/1604.07316.pdf

  23. https://github.com/shikorab/tf-faster-rcnn

  24. https://github.com/TSchattschneider/PointCNN/commit/1827a79b2ede15007a06d327d95f10bc0753420

  25. Note that Hindle et al.’s meta program change category is unrelated to the field of Metaprogramming (https://en.wikipedia.org/wiki/Metaprogramming).

  26. https://github.com/SAILResearch/suppmaterial-22-aaditya-ml_change_taxonomy

  27. https://github.com/SAILResearch/suppmaterial-22-aaditya-ml_change_taxonomy

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Acknowledgements

We thank Greg Wilson for providing insightful ideas and comments for this work. We also thank Boyuan Chen, Minke Xiu, Javier Rosales and Wanqing Li for their contributions to the analysis and feedback on this work.

Funding

This research is partially supported by the NSERC grants RGPIN-2019-06014 and RGPAS-2019-00075.

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Authors and Affiliations

  1. Queen’s University, Kingston, ON, Canada

    Aaditya Bhatia & Bram Adams

  2. University of Cape Coast, Cape Coast, Ghana

    Ellis E. Eghan

  3. VibroSyst Inc., Montreal, Canada

    Manel Grichi

  4. Polytechnique Montreal, Montreal, Canada

    William G. Cavanagh

  5. York University, Toronto, Canada

    Zhen Ming (Jack) Jiang

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  1. Aaditya Bhatia
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  2. Ellis E. Eghan
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Correspondence to Aaditya Bhatia.

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Bhatia, A., Eghan, E.E., Grichi, M. et al. Towards a change taxonomy for machine learning pipelines. Empir Software Eng 28, 60 (2023). https://doi.org/10.1007/s10664-022-10282-8

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