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.

软件维护和发展涉及软件项目成功的关键活动。为了支持此类活动并保持代码最新且无错误，软件社区使用了问题跟踪程序，即用于发信号，处理和解决软件系统中发生的问题的工具。但是，在受欢迎的项目中，每天都会提交数十或数百个问题报告。在这种情况下，识别每个提交的报告的类型（例如，错误报告，功能请求等）将有助于管理和确定要解决的问题的优先级。为了支持问题处理活动，在本文中，我们提出了票务提示器，这是一个GitHub应用程序，它通过机器学习技术来分析问题的标题和描述，以自动识别在GitHub上提交的报告的类型，并为每个问题分配标签。我们通过经验评估了该工具在大约30,000个GitHub问题上的预测性能。我们的结果表明，Ticket Tagger可以以合理的高效率识别正确的标签以分配给GitHub问题。考虑到这些结果以及该工具旨在轻松集成到GitHub问题管理流程中的事实，Ticket Tagger为开发人员提供了一个有用的解决方案。