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. Instead, they use different metrics in the training and testing phases, which can negatively impact the model performance in retrieval tasks. In this paper, we propose RLocator, a Reinforcement Learning-based (RL) bug localization approach. We formulate the bug localization problem 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. Our evaluation shows that RLocator achieves up to a Mean Reciprocal Rank (MRR) of 0.62 and a Mean Average Precision (MAP) of 0.59. Our results demonstrate that directly optimizing evaluation measures considerably contributes to performance improvement of the bug localization problem.

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