RLocator: Reinforcement Learning for Bug Localization
- URL: http://arxiv.org/abs/2305.05586v2
- Date: Fri, 2 Jun 2023 18:43:17 GMT
- Title: RLocator: Reinforcement Learning for Bug Localization
- Authors: Partha Chakraborty, Mahmoud Alfadel, and Meiyappan Nagappan
- Abstract summary: We present RLocator, a Reinforcement Learning-based bug localization approach.
We experimentally evaluate it based on a benchmark dataset of 8,316 bug reports from six popular Apache projects.
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.
- Score: 1.9174135233947958
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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
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.
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