Document Type
Conference Proceeding
Publication Date
7-2-2024
Publication Title
MSR '24: Proceedings of the 21st International Conference on Mining Software Repositories
Pages
431 - 443
Publisher Name
ACM
Abstract
The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse. This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs with over 50 monthly downloads (14,296 PTMs), along with 28,575 open-source software repositories from GitHub that utilize these models. Additionally, the dataset includes 44,337 mappings from 15,129 downstream GitHub repositories to the 2,530 PTMs they use. To enhance the dataset’s comprehensiveness, we developed prompts for a large language model to automatically extract model metadata, including the model’s training datasets, parameters, and evaluation metrics. Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation. Our example application reveals inconsistencies in software licenses across PTMs and their dependent projects. PeaTMOSS lays the foundation for future research, offering rich opportunities to investigate the PTM supply chain. We outline mining opportunities on PTMs, their downstream usage, and crosscutting questions.
Our artifact is available at https://github.com/PurdueDualityLab/PeaTMOSS-Artifact.
Recommended Citation
Wenxin Jiang, Jerin Yasmin, Jason Jones, Nicholas Synovic, Jiashen Kuo, Nathaniel Bielanski, Yuan Tian, George K. Thiruvathukal, and James C. Davis, PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software", In Proceedings of Mining Software Repositories 2024 (MSR 2024).
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Statement
© The Author(s), 2024.
Comments
Author Posting © The Author(s), 2024. This proceeding is posted here by permission of ACM for personal use and redistribution. This article was published open access in MSR '24: Proceedings of the 21st International Conference on Mining Software Repositories, July 2, 2024 at https://doi.org/10.1145/3643991.3644907