A Multi-label Classification Bot for Issue Management System in GitHub

Doje Park, Yyejin Yang, Gwang Choi, Seonah Lee, Sungwon Kang

http://doi.org/10.5626/JOK.2021.48.8.928

The GitHub platform, on which many developers develop open-source software projects, provides an issue management system. Using the system, the stakeholders can report software problems or functional requests as issues. The issue management system provides issue report forms and allows developers to create and use labels to classify issues. However, since the labeling work is manually done, it requires a lot of effort from the developers and inaccurate labeling can easily occur. In addition, it takes a lot of time for a project manager to read and give feedback on each issue. To mitigate this problem, previous studies have proposed attaching a single label to an issue automatically. However, in practice, there are a number of issue reports that need multiple labels to be attached. Therefore, in this study, we proposed a multi-labeling bot that automatically attaches multiple labels to an issue report in order to reduce the effort required by a project manager to read issue reports and give feedback in GitHub. The multi-label classification of our bot showed F-score ranging from 0.54 to 0.78.