Multi-View Adaptive Contrastive Learning for Information Retrieval Based Fault Localization
<|reference_start|>Multi-View Adaptive Contrastive Learning for Information Retrieval Based Fault Localization: Most studies focused on information retrieval-based techniques for fault localization, which built representations for bug reports and source code files and matched their semantic vectors through similarity measurement. However, such approaches often ignore some useful information that might help improve localization performance, such as 1) the interaction relationship between bug reports and source code files; 2) the similarity relationship between bug reports; and 3) the co-citation relationship between source code files. In this paper, we propose a novel approach named Multi-View Adaptive Contrastive Learning for Information Retrieval Fault Localization (MACL-IRFL) to learn the above-mentioned relationships for software fault localization. Specifically, we first generate data augmentations from report-code interaction view, report-report similarity view and code-code co-citation view separately, and adopt graph neural network to aggregate the information of bug reports or source code files from the three views in the embedding process. Moreover, we perform contrastive learning across these views. Our design of contrastive learning task will force the bug report representations to encode information shared by report-report and report-code views,and the source code file representations shared by code-code and report-code views, thereby alleviating the noise from auxiliary information. Finally, to evaluate the performance of our approach, we conduct extensive experiments on five open-source Java projects. The results show that our model can improve over the best baseline up to 28.93%, 25.57% and 20.35% on Accuracy@1, MAP and MRR, respectively.<|reference_end|>
@article{zhou2024multi-view,
    title={Multi-View Adaptive Contrastive Learning for Information Retrieval Based
  Fault Localization},
    author={Chunying Zhou, Xiaoyuan Xie, Gong Chen, Peng He, Bing Li},
    journal={arXiv preprint arXiv:2409.12519},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12519},
    primaryClass={cs.SE cs.IR}
}