  Software maintenance and evolution involves critical activities for the
success of software projects. To support such activities and keep code
up-to-date and error-free, software communities make use of issue trackers,
i.e., tools for signaling, handling, and addressing the issues occurring in
software systems. However, in popular projects, tens or hundreds of issue
reports are daily submitted. In this context, identifying the type of each
submitted report (e.g., bug report, feature request, etc.) would facilitate the
management and the prioritization of the issues to address. To support issue
handling activities, in this paper, we propose Ticket Tagger, a GitHub app
analyzing the issue title and description through machine learning techniques
to automatically recognize the types of reports submitted on GitHub and assign
labels to each issue accordingly. We empirically evaluated the tool's
prediction performance on about 30,000 GitHub issues. Our results show that the
Ticket Tagger can identify the correct labels to assign to GitHub issues with
reasonably high effectiveness. Considering these results and the fact that the
tool is designed to be easily integrated in the GitHub issue management
process, Ticket Tagger consists in a useful solution for developers.
