GraphLocator: Graph-Guided Causal Reasoning for Issue Localization

The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in automated software engineering due to the semantic gap between issue description and source code implementation. This gap manifests as two mismatches: (1) symptom–to-cause mismatches, where descriptions do not explicitly reveal underlying root causes; (2) one-to-many mismatches, where a single issue corresponds to multiple interdependent code entities. To address these two mismatches, we propose GraphLocator, an LLM-based approach that mitigates symptom–to-cause mismatches through causal structure discovering and resolves one-to-many mismatches via dynamic issue disentangling. The key artifact of GraphLocator is the causal issue graph(CIG), in which vertices represent discovered sub-issues along with their associated code entities, and edges encode the causal dependencies between them. The workflow of GraphLocator consists of two phases: symptom vertices locating and dynamic CIG discovering; it first identifies symptom locations on the repository graph, then dynamically expands the CIG by iteratively reasoning over neighboring vertices, discovering new sub-issues and updating causal dependencies. Experiments on three real-world Python and Java datasets demonstrates the effectiveness of GraphLocator: (1) Compared with baselines, GraphLocator achieves more accurate localization with average improvements of +19.49% in function-level recall and +11.89% in precision with acceptable overhead. (2) GraphLocator outperforms baselines on both symptom-to-cause and one-to-many mismatch scenarios, achieving recall improvement of +16.44% and +19.18%, precision improvement of +7.78% and +13.23%, respectively. (3) The disentangled causal structure CIG generated by GraphLocator yields the highest relative improvement, resulting in a 28.74% increase in performance on the downstream issue-resolving task.

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2026-06-30
DOI
https://doi.org/10.1145/3797079
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
0.00

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article

GraphLocator: Graph-Guided Causal Reasoning for Issue Localization

Zhi Jin, Aofan Liu, Wei Zhang, Pengfei Gao et al.
Proceedings of the ACM on software engineering.
Software Engineering Research
article

GraphLocator: Graph-Guided Causal Reasoning for Issue Localization

Zhi Jin, Aofan Liu, Wei Zhang, Pengfei Gao, Hantao Zhao, Chao Peng, Wei Liu
article en

Abstract

The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in automated software engineering due to the semantic gap between issue description and source code implementation. This gap manifests as two mismatches: (1) symptom–to-cause mismatches, where descriptions do not explicitly reveal underlying root causes; (2) one-to-many mismatches, where a single issue corresponds to multiple interdependent code entities. To address these two mismatches, we propose GraphLocator, an LLM-based approach that mitigates symptom–to-cause mismatches through causal structure discovering and resolves one-to-many mismatches via dynamic issue disentangling. The key artifact of GraphLocator is the causal issue graph(CIG), in which vertices represent discovered sub-issues along with their associated code entities, and edges encode the causal dependencies between them. The workflow of GraphLocator consists of two phases: symptom vertices locating and dynamic CIG discovering; it first identifies symptom locations on the repository graph, then dynamically expands the CIG by iteratively reasoning over neighboring vertices, discovering new sub-issues and updating causal dependencies. Experiments on three real-world Python and Java datasets demonstrates the effectiveness of GraphLocator: (1) Compared with baselines, GraphLocator achieves more accurate localization with average improvements of +19.49% in function-level recall and +11.89% in precision with acceptable overhead. (2) GraphLocator outperforms baselines on both symptom-to-cause and one-to-many mismatch scenarios, achieving recall improvement of +16.44% and +19.18%, precision improvement of +7.78% and +13.23%, respectively. (3) The disentangled causal structure CIG generated by GraphLocator yields the highest relative improvement, resulting in a 28.74% increase in performance on the downstream issue-resolving task.

Proceedings of the ACM on software engineering.Vol. 3(FSE)
Peking University (CN), Wuhan University (CN), Institute of Software (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 3%
Software Engineering Research
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