RLocator: Reinforcement Learning for Bug Localization

IF 6.5 1区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

IEEE Transactions on Software Engineering Pub Date : 2024-08-30 DOI:10.1109/TSE.2024.3452595

Partha Chakraborty;Mahmoud Alfadel;Meiyappan Nagappan

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引用次数: 0

Abstract

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.

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RLocator：用于错误定位的强化学习

软件开发人员需要花费大量时间来修复项目中的错误。为了简化这一过程，有人提出了错误定位方法，以识别可能导致特定错误的源代码文件。之前的工作提出了几种基于相似性的机器学习技术，用于错误定位。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指标。我们认为，直接优化评估指标可以积极提高错误定位方法的性能。因此，在本文中，我们利用强化学习（RL）技术来直接优化排名指标。我们提出了基于强化学习的错误定位方法 RLocator。我们使用马尔可夫决策过程（MDP）来制定 RLocator，以直接优化评估指标。我们介绍了该技术，并基于一个基准数据集对其进行了实验性评估，该数据集包含来自 6 个非常流行的 Apache 项目的 8,316 份错误报告。评估结果表明，RLocator 的平均互易等级 (MRR) 为 0.62，平均精度 (MAP) 为 0.59，前 1 名得分为 0.46。我们将 RLocator 与 FLIM、BugLocator 和 BL-GAN 这三种最先进的错误定位工具进行了比较。我们的评估结果表明，RLocator 的 MAP、MRR 和 Top K 指标分别提高了 38.3%、36.73% 和 23.68%，远远超过了这两种方法。这些发现突出表明，直接优化评估指标大大有助于提高错误定位问题的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Software Engineering 工程技术-工程：电子与电气

CiteScore

9.70

自引率

10.80%

发文量

724

审稿时长

6 months

期刊介绍： IEEE Transactions on Software Engineering seeks contributions comprising well-defined theoretical results and empirical studies with potential impacts on software construction, analysis, or management. The scope of this Transactions extends from fundamental mechanisms to the development of principles and their application in specific environments. Specific topic areas include: a) Development and maintenance methods and models: Techniques and principles for specifying, designing, and implementing software systems, encompassing notations and process models. b) Assessment methods: Software tests, validation, reliability models, test and diagnosis procedures, software redundancy, design for error control, and measurements and evaluation of process and product aspects. c) Software project management: Productivity factors, cost models, schedule and organizational issues, and standards. d) Tools and environments: Specific tools, integrated tool environments, associated architectures, databases, and parallel and distributed processing issues. e) System issues: Hardware-software trade-offs. f) State-of-the-art surveys: Syntheses and comprehensive reviews of the historical development within specific areas of interest.