Research Paper#Software Engineering, AI, Graph Neural Networks, Causal Reasoning🔬 ResearchAnalyzed: Jan 3, 2026 20:01
GraphLocator: Causal Reasoning for Issue Localization in Software
Published:Dec 27, 2025 05:02
•1 min read
•ArXiv
Analysis
This paper introduces GraphLocator, a novel approach to issue localization in software engineering. It addresses the challenges of symptom-to-cause and one-to-many mismatches by leveraging causal reasoning and graph structures. The use of a Causal Issue Graph (CIG) is a key innovation, allowing for dynamic issue disentangling and improved localization accuracy. The experimental results demonstrate significant improvements over existing baselines, highlighting the effectiveness of the proposed method in both recall and precision, especially in scenarios with symptom-to-cause and one-to-many mismatches. The paper's contribution lies in its graph-guided causal reasoning framework, which provides a more nuanced and accurate approach to issue localization.
Key Takeaways
- •GraphLocator uses a causal issue graph (CIG) to model causal dependencies between sub-issues and code entities.
- •It addresses symptom-to-cause and one-to-many mismatches in issue localization.
- •Experiments show significant improvements in recall and precision compared to baselines.
- •The CIG improves performance on downstream resolving tasks.
Reference
“GraphLocator achieves more accurate localization with average improvements of +19.49% in function-level recall and +11.89% in precision.”