mlsanityData to 7 Oct 2026

RLocator: Reinforcement Learning for Bug Localization

arXiv 2305.05586 ↗First posted 9 May 2023cs.SE cs.AIcited by 7

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Abstract (arXiv)

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 two state-of-the-art bug localization tools, FLIM and BugLocator. 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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 25 of its 90 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

25 of 90 references.

References · 25

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13 more
  1. 2018Improving Automatic Source Code Summarization via Deep Reinforcement Learningcited by 99
  2. 2019Deep Reinforcement Learning for Cyber Securitycited by 92
  3. 2018Evolutionary Generative Adversarial Networkscited by 42
  4. 2021Fast Changeset-based Bug Localization with BERTcited by 13
  5. 2020Reinforcement Learning for Test Case Prioritizationcited by 12
  6. 2018Improving IR-Based Bug Localization with Context-Aware Query Reformulationcited by 11
  7. 2023Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspectivecited by 8
  8. 2021GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learningcited by 7
  9. 2020CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Wordscited by 5
  10. 2021The Forgotten Role of Search Queries in IR-based Bug Localization: An Empirical Studycited by 5
  11. 2020Balancing Reinforcement Learning Training Experiences in Interactive Information Retrievalcited by 4
  12. 2021GloBug: Using Global Data in Fault Localizationcited by 2
  13. 2021Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learningcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2024BLAZE: Cross-Language and Cross-Project Bug Localization via Dynamic Chunking and Hard Example Learningcited by 7
  2. 2025Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detectioncited by 7
  3. 2024Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasetscited by 6
  4. 2025Improved IR-based Bug Localization with Intelligent Relevance Feedbackcited by 5
  5. 2025Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agentcited by 1
  6. 2025From Benchmark Data To Applicable Program Repair: An Experience Reportcited by 0
  7. 2026From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learningcited by 0