2024/08/30 by Partha Chakraborty, Mahmoud Alfadel, Meiyappan Nagappan · 15 citations
Computer Science · #Artificial intelligence #Computer science #Human–computer interaction #Machine learning #Multi-Agent Systems and Negotiation #Programming language #Reinforcement Learning in Robotics #Reinforcement learning #Software #Software Engineering Research #Software bug #Software engineering
paper · doi:10.1109/tse.2024.3452595
published in IEEE Transactions on Software Engineering 50(10), 2695-2708 (IEEE Computer Society)
openalex publication_date 2024/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Software developers spend a significant portion of time fixing bugs in their projects. To streamline this process, bug localization approaches have been proposed to identify the source code files that are likely responsible for a particular bug. Prior work proposed several similarity-based machine-learning techniques for bug localization. Despite significant advances in these techniques, they do not directly optimize the evaluation measures. We argue that directly optimizing evaluation measures can positively contribute to the performance of bug localization approaches. Therefore, in this paper, we utilize Reinforcement Learning (RL) techniques to directly optimize the ranking metrics. We proposeRLocator, a Reinforcement Learning-based bug localization approach. We formulate RLocator using a Markov Decision Process (MDP) to optimize the evaluation measures directly. We present the technique and experimentally evaluate it based on a benchmark dataset of 8,316 bug reports from six highly popular Apache projects. The results of our evaluation reveal that RLocator achieves a Mean Reciprocal Rank (MRR) of 0.62, a Mean Average Precision (MAP) of 0.59, and a Top 1 score of 0.46. We compare RLocator with three state-of-the-art bug localization tools, FLIM, BugLocator, and BL-GAN. Our evaluation reveals that RLocator outperforms both approaches by a substantial margin, with improvements of 38.3% in MAP, 36.73% in MRR, and 23.68% in the Top K metric. These findings highlight that directly optimizing evaluation measures considerably contributes to performance improvement of the bug localization problem.