PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software
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 cross- cutting questions.
Mon 15 AprDisplayed time zone: Lisbon change
16:00 - 17:30 | Machine learning for Software EngineeringTechnical Papers at Grande Auditório Chair(s): Diego Costa Concordia University, Canada | ||
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16:24 12mTalk | MicroRec: Leveraging Large Language Models for Microservice Recommendation Technical Papers Ahmed Saeed Alsayed University of Wollongong, Hoa Khanh Dam University of Wollongong, Chau Nguyen University of Wollongong | ||
16:36 12mTalk | PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software Technical Papers Wenxin Jiang Purdue University, Jerin Yasmin Queen's University, Canada, Jason Jones Purdue University, Nicholas Synovic Loyola University Chicago, Jiashen Kuo Purdue University, Nathaniel Bielanski Purdue University, Yuan Tian Queen's University, Kingston, Ontario, George K. Thiruvathukal Loyola University Chicago and Argonne National Laboratory, James C. Davis Purdue University DOI Pre-print | ||
16:48 12mTalk | Data Augmentation for Supervised Code Translation Learning Technical Papers Binger Chen Technische Universität Berlin, Jacek golebiowski Amazon AWS, Ziawasch Abedjan Leibniz Universität Hannover | ||
17:00 12mTalk | On the Effectiveness of Machine Learning-based Call-Graph Pruning: An Empirical Study Technical Papers Amir Mir Delft University of Technology, Mehdi Keshani Delft University of Technology, Sebastian Proksch Delft University of Technology Pre-print | ||
17:12 12mTalk | Leveraging GPT-like LLMs to Automate Issue Labeling Technical Papers Giuseppe Colavito University of Bari, Italy, Filippo Lanubile University of Bari, Nicole Novielli University of Bari, Luigi Quaranta University of Bari, Italy Pre-print | ||