Explaining Notable Metadata Practices in PyPI Libraries: An Empirical Study about Repository and Donation Platform URLs
For OSS maintainers and tool developers, this work identifies root causes of metadata deficiencies in PyPI, enabling targeted improvements to dependency monitoring and project sustainability.
This study explains why PyPI libraries have incomplete or outdated metadata, particularly repository and donation platform URLs, finding that missing links are due to oversight, lack of awareness, or perceived irrelevance, while platform dominance is driven by ideological, technical, and organizational factors. The LLM-based topic modeling approach achieved up to 88% lexical and 92% semantic similarity, with 77-78% of topics meeting all quality criteria.
Background: Open source software (OSS) libraries are critical components of modern software systems, yet their metadata-particularly links to source code repositories and donation platforms-is often incomplete, outdated, or inconsistent. Such deficiencies hinder dependency monitoring, security assessment, and the sustainability of OSS projects. Aims: This study aims to explain notable metadata practices in PyPI libraries, focusing on platform dominance, outdated links, and missing references to repositories and donation platforms. As this investigation relies on large-scale qualitative survey data, we further evaluate the robustness and quality of the LLM-based topic modeling approach used to derive the findings. Method: We conducted two surveys targeting PyPI authors and maintainers, collecting 1,776 open-ended responses. To analyze these responses, we developed a LLM-based topic modeling pipeline using LLaMA 3.3 70B, including preprocessing, topic extraction, and topic merging. Robustness was assessed across 30 repeated runs using Jaccard and cosine similarity, while topic quality was evaluated by 23 experts using a structured assessment framework and Randolph's Kappa. Results: The findings reveal that missing or outdated repository links are primarily associated with oversight, lack of awareness, or perceived irrelevance, while platform dominance is driven by ideological, technical, and organizational factors. Donation platform links are often omitted due to skepticism, limited perceived benefit, or lack of knowledge, and are preferentially placed on GitHub for visibility reasons. The topic modeling approach demonstrated high robustness (up to 88% lexical and 92% semantic similarity) and produced high-quality topics, with approximately 77-78% meeting all evaluation criteria and moderate inter-rater agreement.