Background: Collaborative Software Package Registries (SPRs) are an integral
part of the software supply chain. Much engineering work synthesizes SPR
package into applications. Prior research has examined SPRs for 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: Recent empirical research has examined PTM registries in ways such as
vulnerabilities, reuse processes, and evolution. However, no existing research
synthesizes them to provide a systematic understanding of the current
knowledge. Some of the existing research includes qualitative claims lacking
quantitative analysis. Our research fills these gaps by providing a knowledge
synthesis and quantitative analyses.
Methods: We first conduct a systematic literature review (SLR). We then
observe that some of the claims are qualitative. We identify quantifiable
metrics associated 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 findings are: (1) PTMs have a much 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: We confirm qualitative research claims with concrete metrics,
supporting prior qualitative and case study research. Our measures show further
dynamics of PTM reuse, inspiring research infrastructure and new measures.

我们通过系统文献综述和定量分析验证了关于 Pre-Trained Model （PTM）的重复使用的定性研究结论，证实 PTM 具有比传统软件更高的更新速度，并且文档质量与 PTM 的受欢迎程度存在强相关性。