  With the rapid development of AI technologies, thousands of AI papers are
being published each year. Many of these papers have released sample code to
facilitate follow-up researchers. This paper presents an explorative study of
over 1700 code repositories of AI papers hosted on GitHub. We find that these
repositories are often poorly written, lack of documents, lack of maintenance,
and hard to configure the underlying runtime environment. Thus, many code
repositories become inactive and abandoned. Such a situation makes follow-up
researchers hard to reproduce the results or do further research. In addition,
these hard-to-reuse code makes a gap between academia and industry. Based on
the findings, we give some recommendations on how to improve the quality of
code repositories of AI papers.
