SEOct 6, 2020

What Makes a Popular Academic AI Repository?

arXiv:2010.02472v233 citations
AI Analysis

This work provides actionable insights for AI researchers to improve the visibility and usability of their code repositories, though it is incremental as it builds on existing studies of software popularity.

The study analyzed 1,149 academic AI repositories on GitHub to identify software engineering practices that correlate with popularity, finding that popular and unpopular repositories differ significantly in 11 out of 21 features, with key factors including links to other repositories, images in README files, and license inclusion.

Many AI researchers are publishing code, data and other resources that accompany their papers in GitHub repositories. In this paper, we refer to these repositories as academic AI repositories. Our preliminary study shows that highly cited papers are more likely to have popular academic AI repositories (and vice versa). Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers. We collect 1,149 academic AI repositories, in which we label the top 20% repositories that have the most number of stars as popular, and we label the bottom 70% repositories as unpopular. The remaining 10% repositories are set as a gap between popular and unpopular academic AI repositories. We propose 21 features to characterize the software engineering practices of academic AI repositories. Our experimental results show that popular and unpopular academic AI repositories are statistically significantly different in 11 of the studied features---indicating that the two groups of repositories have significantly different software engineering practices. Furthermore, we find that the number of links to other GitHub repositories in the README file, the number of images in the README file and the inclusion of a license are the most important features for differentiating the two groups of academic AI repositories. Our dataset and code are made publicly available to share with the community.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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