# RLocator: Reinforcement Learning for Bug Localization

arXiv: https://arxiv.org/abs/2305.05586 · HTML: https://mlsanity.com/paper/2305.05586/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 90 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Reinforcement Learning in Healthcare: A Survey](https://mlsanity.com/paper/1908.08796/) (2019)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Understanding the impact of entropy on policy optimization](https://mlsanity.com/paper/1811.11214/) (2018)
- [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/1803.00657/) (2018)
- [Reinforcement Learning for Test Case Prioritization](https://mlsanity.com/paper/2011.01834/) (2020)
- [Fast Changeset-based Bug Localization with BERT](https://mlsanity.com/paper/2112.14169/) (2021)
- [Improving IR-Based Bug Localization with Context-Aware Query Reformulation](https://mlsanity.com/paper/1808.00594/) (2018)
- [Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2302.03561/) (2023)
- [GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning](https://mlsanity.com/paper/2106.06232/) (2021)
- [CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words](https://mlsanity.com/paper/2005.14373/) (2020)
- [The Forgotten Role of Search Queries in IR-based Bug Localization: An Empirical Study](https://mlsanity.com/paper/2108.05341/) (2021)
- [Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval](https://mlsanity.com/paper/2006.03185/) (2020)
- [GloBug: Using Global Data in Fault Localization](https://mlsanity.com/paper/2101.05862/) (2021)
- [Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning](https://mlsanity.com/paper/2108.10533/) (2021)

## Built on by (most cited first, 7 of 7)

- [BLAZE: Cross-Language and Cross-Project Bug Localization via Dynamic Chunking and Hard Example Learning](https://mlsanity.com/paper/2407.17631/) (2024)
- [Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2506.18245/) (2025)
- [Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets](https://mlsanity.com/paper/2407.03093/) (2024)
- [Improved IR-based Bug Localization with Intelligent Relevance Feedback](https://mlsanity.com/paper/2501.10542/) (2025)
- [Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent](https://mlsanity.com/paper/2512.14990/) (2025)
- [From Benchmark Data To Applicable Program Repair: An Experience Report](https://mlsanity.com/paper/2508.16071/) (2025)
- [From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2609.38402/) (2026)

## 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.