Background: Software Package Registries (SPRs) are an integral part of the software supply chain. These collaborative platforms unite contributors, users, and packages, and they streamline package management. Much engineering work focuses on synthesizing packages from SPRs into a downstream project. Prior work has thoroughly characterized the SPRs associated with traditional software, such as NPM (JavaScript) and PyPI (Python). Pre-Trained Model (PTM) Registries are an emerging class of SPR of increasing importance, because they support the deep learning supply chain.

Aims: A growing body of empirical research has examined PTM registries from various angles, such as vulnerabilities, reuse processes, and evolution. However, no existing research synthesizes them to provide a systematic understanding of the current knowledge. Furthermore, much of the existing research includes unsupported qualitative claims and lacks sufficient quantitative analysis. Our research aims to fill these gaps by providing a thorough knowledge synthesis and use it to inform further quantitative analysis.

Methods: To consolidate existing knowledge on PTM reuse, we first conduct a systematic literature review (SLR). We then observe that some of the claims are qualitative and lack quantitative evidence. We identify quantifiable metrics assoiated with those claims, and measure in order to substantiate these claims. Results: From our SLR, we identify 12 claims about PTM reuse on the HuggingFace platform, 4 of which lack quantitative validation. We successfully test 3 of these claims through a quantitative analysis, and directly compare one with traditional software. Our findings corroborate qualitative claims with quantitative measurements. Our two most notable findings are: (1) PTMs have a significantly higher turnover rate than traditional software, indicating a dynamic and rapidly evolving reuse environment within the PTM ecosystem; and (2) There is a strong correlation between documentation quality and PTM popularity.

Conclusions: Our findings validate several qualitative research claims with concrete metrics, confirming prior qualitative and case study research. Our measures show further dynamics of PTM reuse, motivating further research infrastructure and new kinds of measurements.