  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.
