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An Empirical Study On Correlation between Readme Content and Project Popularity

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arxiv 2206.10772 v1 pith:ONULK2QW submitted 2022-06-21 cs.SE

classification cs.SE
keywords readmecontentpopularityfilesprojectcorrelationprojectsaspects
verification ladder T0 review T1 audit T2 compute T3 formal
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Readme in GitHub repositories serves as a preliminary source of information, and thus helps developers in understanding about the projects, for reuse or extension. Different types of contextual and structural content, which we refer to as categories of the content and features in the content respectively, are present in readme files, and could determine the extent of comprehension about project. Consequently, the structural and contextual aspects of the content could impact the project popularity. Studying the correlation between the content and project popularity could help in focusing on the aspects that could improve popularity, while designing the readme files. However, existing studies explore the categories of content and types of features in readme files, and do not explore their usefulness towards project popularity. Hence, we present an empirical study to understand correlation between readme file content and project popularity. We perform the study on 1950 readme files of public GitHub projects, spanning across ten programming languages, and observe that readme files in majority of the popular projects are well organised using lists and images, and comprise links to external sources. Also, repositories with readme files containing contribution guidelines and references were observed to be associated with higher popularity.

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  1. Enhancing COBOL Code Explanations: A Multi-Agents Approach Using Large Language Models

    cs.SE 2025-07 conditional novelty 4.0 of 10

    A two-agent LLM pipeline with hierarchical merging explains COBOL functions, files, and projects, outperforming zero-shot baselines on several text-quality metrics.

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