Mining Software Repositories for Expert Recommendation
This work addresses the challenge of efficiently assigning bugs to developers in open-source software, which is incremental as it builds on existing topic modeling and mining techniques.
The paper tackles the problem of automated bug assignment in large open-source software projects by proposing an approach that uses BERTopic and TopicMiner techniques on bug report features like products, components, priority, and severity, achieving results compared to prior methods such as TopicMiner MTM and BUGZIE using Top-k accuracy on Eclipse and Mozilla projects.
We propose an automated approach to bug assignment to developers in large open-source software projects. This way, we assist human bug triagers who are in charge of finding the best developer with the right level of expertise in a particular area to be assigned to a newly reported issue. Our approach is based on the history of software development as documented in the issue tracking systems. We deploy BERTopic and techniques from TopicMiner. Our approach works based on the bug reports' features, such as the corresponding products and components, as well as their priority and severity levels. We sort developers based on their experience with specific combinations of new reports. The evaluation is performed using Top-k accuracy, and the results are compared with the reported results in prior work, namely TopicMiner MTM, BUGZIE, Bug triaging via deep Reinforcement Learning BT-RL, and LDA-SVM. The evaluation data come from various Eclipse and Mozilla projects, such as JDT, Firefox, and Thunderbird.