PeaTMOSS: Mining Pre-Trained Models in Open-Source Software

Wenxin Jiang, Jason Jones, Jerin Yasmin, Nicholas Synovic, Rajeev Sashti +4 more
10/5/2023
cs.SEcs.AI

Official code: PurdueDualityLab/PeaTMOSS-Demos (13 stars, last commit 2023-10-07).

Abstract

Developing and training deep learning models is expensive, so software engineers have begun to reuse pre-trained deep learning models (PTMs) and fine-tune them for downstream tasks. Despite the wide-spread use of PTMs, we know little about the corresponding software engineering behaviors and challenges. To enable the study of software engineering with PTMs, we present the PeaTMOSS dataset: Pre-Trained Models in Open-Source Software. PeaTMOSS has three parts: a snapshot of (1) 281,638 PTMs, (2) 27,270 open-source software repositories that use PTMs, and (3) a mapping between PTMs and the projects that use them. We challenge PeaTMOSS miners to discover software engineering practices around PTMs. A demo and link to the full dataset are available at: https://github.com/PurdueDualityLab/PeaTMOSS-Demos.

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Code Implementations(1)

PurdueDualityLab/PeaTMOSS-DemosOfficial100%

A repository that holds demos on interactions with the PeaTMOSS Dataset. Suitable for mining software repositories/MSR research on how machine learning and pre-trained models are integrated into (open-source) software engineering.

134PythonAug 15, 20233 years ago
deeplearningmachinelearningpretrained-modelsresearchsoftware-engineering

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Cite this paper

@article{jiang2023peatmoss,
  title  = {PeaTMOSS: Mining Pre-Trained Models in Open-Source Software},
  author = {Wenxin Jiang and Jason Jones and Jerin Yasmin and Nicholas Synovic and Rajeev Sashti and Sophie Chen and George K. Thiruvathukal and Yuan Tian and James C. Davis},
  year   = {2023},
  eprint = {2310.03620},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.03620v1}
}

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