Multi-view Adaptive Contrastive Learning For Information Retrieval Based Fault Localization

Abstract

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 cod

Stats

• citations1
• S2 citations—
• github stars0
• HF likes0
• heat score2.26
• arxiv keyzhou2024multi

Related papers

• Clnet: Cross-view Correspondence Makes A Stronger Geo-localizationer (2025)0.00
• NCL-CIR: Noise-aware Contrastive Learning For Composed Image Retrieval (2025)2.26
• MARC: Multi-label Adaptive Retrieval Contrastive Loss For Remote Sensing Images (2025)0.00
• Recall: Recalibrating Capability Degradation For Mllm-based Composed Image Retrieval (2026)2.90
• Benchmarking Robustness Of Contrastive Learning Models For Medical Image-report Retrieval (2025)0.00
• Domain-invariant Similarity Activation Map Contrastive Learning For Retrieval-based Long-term Visual Localization (2020)13.72
• Geometry-aware CLIP Retrieval Via Local Cross-modal Alignment And Steering (2026)0.00
• X-TRA: Improving Chest X-ray Tasks With Cross-modal Retrieval Augmentation (2023)8.09