Machine learning models forecast future OpenSSF Maintained scores on PyPI-linked GitHub repos with accuracies above 0.95 for bucketed maintenance levels and 0.79 for trend categories.
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PyPI metadata gaps arise mainly from oversight, skepticism, and platform preferences, as shown by surveys of 1,776 responses analyzed with a robust LLaMA-based topic model.
79.1% of PyPI libraries provide at least one valid email address, primarily from PyPI metadata, with high coverage extending to dependency chains.
citing papers explorer
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Forecasting the Maintained Score from the OpenSSF Scorecard: A Study of GitHub Repositories Linked to PyPI Packages
Machine learning models forecast future OpenSSF Maintained scores on PyPI-linked GitHub repos with accuracies above 0.95 for bucketed maintenance levels and 0.79 for trend categories.
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Investigating Notable Metadata Practices in PyPI Libraries: An Empirical Study about Repository and Donation Platform URLs
PyPI metadata gaps arise mainly from oversight, skepticism, and platform preferences, as shown by surveys of 1,776 responses analyzed with a robust LLaMA-based topic model.
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Analyzing the Availability of E-Mail Addresses for PyPI Libraries
79.1% of PyPI libraries provide at least one valid email address, primarily from PyPI metadata, with high coverage extending to dependency chains.