Linking Source Code to Untangled Change Intents

Xiaoyu Liu, LiGuo Huang, Chuanyi Liu, Vincent Ng

Abstract: Previous work [13] suggests that tangled changes (i.e., different change intents aggregated in one single commit message) could complicate tracing to different change tasks when developers manage software changes. Identifying links from changed source code to untangled change intents could help developers solve this problem. Manually identifying such links requires lots of experience and review efforts, however. Unfortunately, there is no automatic method that provides this capability. In this paper, we propose AutoCILink, which automatically identifies code to untangled change intent links with a pattern-based link identification system (AutoCILink-P) and a supervised learning-based link classification system (AutoCILink-ML). Evaluation results demonstrate the effectiveness of both systems: the pattern-based AutoCILink-P and the supervised learning-based AutoCILink-ML achieve average accuracy of 74.6% and 81.2%, respectively.