A Tale of Two Tasks: Automated Issue Priority Prediction with Deep Multi-task Learning
Yingling Li, Xing Che, Yue-Kai Huang, Junjie Wang, Song Wang, Yawen Wang, Qing Wang · 2022
Background. Issues are prevalent, and identifying the correct priority of the reported issues is crucial to reduce the maintenance effort and ensure higher software quality. There are several approaches for the automatic priority prediction, yet they do not fully utilize the related information that might influence the priority assignment. Our observation reveals that there are noticeable correlations between an issue’s priority and its category, e.g., an issue of bug category tends to be assigned with higher priority than an issue of document category. This correlation motivates us to employ multi-task learning to share the knowledge about issue’s category prediction and facilitating priority prediction.