  GitHub is the largest host of open source software on the Internet. This
large, freely accessible database has attracted the attention of practitioners
and researchers alike. But as GitHub's growth continues, it is becoming
increasingly hard to navigate the plethora of repositories which span a wide
range of domains. Past work has shown that taking the application domain into
account is crucial for tasks such as predicting the popularity of a repository
and reasoning about project quality. In this work, we build on a previously
annotated dataset of 5,000 GitHub repositories to design an automated
classifier for categorising repositories by their application domain. The
classifier uses state-of-the-art natural language processing techniques and
machine learning to learn from multiple data sources and catalogue repositories
according to five application domains. We contribute with (1) an automated
classifier that can assign popular repositories to each application domain with
at least 70% precision, (2) an investigation of the approach's performance on
less popular repositories, and (3) a practical application of this approach to
answer how the adoption of software engineering practices differs across
application domains. Our work aims to help the GitHub community identify
repositories of interest and opens promising avenues for future work
investigating differences between repositories from different application
domains.
