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 propose RLocator , 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.

中文翻译：

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RLocator：用于错误定位的强化学习


软件开发人员花费了大量时间修复项目中的错误。为了简化此过程，已经提出了 bug 本地化方法来识别可能导致特定 bug 的源代码文件。之前的工作提出了几种基于相似性的机器学习技术来定位错误。尽管这些技术取得了重大进展，但它们并没有直接优化评估措施。我们认为，直接优化评估措施可以对 bug 定位方法的性能做出积极贡献。因此，在本文中，我们利用强化学习 （RL） 技术直接优化排名指标。我们提出了 RLocator ，一种基于强化学习的错误定位方法。我们使用马尔可夫决策过程 （MDP） 来构建 RLocator 来直接优化评估措施。我们介绍了这项技术，并根据来自 6 个非常受欢迎的 Apache 项目的 8,316 个错误报告的基准数据集对其进行了实验评估。我们的评估结果表明，RLocator 的平均倒数秩 （MRR） 为 0.62，平均精度均值 （MAP） 为 0.59，前 1 分为 0.46。我们将 RLocator 与三种最先进的错误定位工具（FLIM、BugLocator 和 BL-GAN）进行了比较。我们的评估显示，RLocator 的性能大大优于这两种方法，MAP 提高了 38.3%，MRR 提高了 36.73%，Top K 指标提高了 23.68%。这些发现强调，直接优化评估措施对 bug 定位问题的性能改进有很大帮助。