GraMuS: Boosting statement-level fault localization via graph representation and multimodal information

Fault Localization (FL) aims to reduce the cost of manual debugging by highlighting the statements which are more likely responsible for observed failures. However, existing techniques have limited effectiveness in practice due to inflexible suspiciousness evaluations and oversimplified representation of execution information. In this paper, we propose GraMuS, a novel Graph representation learning and Multimodal information based technique for Statement-level FL. GraMuS comprises two key components: a fine-grained fault diagnosis graph and a multi-level collaborative suspiciousness evaluation. The former integrally records enriched multimodal information from various levels of granularity (including methods, statements, and mutants) by a graph structure. The latter utilizes the interactions between FL tasks at various levels of granularity to extract existing/latent useful features from multimodal information for improving FL precision. Empirical studies on the widely used Defects4J(V2.0.0) dataset show that GraMuS can outperform state-of-the-art baselines in both single-fault programs and multiple-fault programs, including one large language models, four learning-based FL techniques, three variable-based FL techniques, 36 spectrum-based FL techniques, and 36 mutation-based FL techniques. In particular, GraMuS can localize 26/29/31 more faulty statements than the state-of-the-art baseline ChatGPT-4/DepGraph/VarDT, in terms of TOP−1 metric. Further investigation shows that the method-level FL task can help GraMuS localize 27 more faulty statements, resulting in a 50.94 % improvement. Finally, we further evaluate GraMuS in 374 Python programs from ConDefects, and find that GraMuS consistently outperforms state-of-the-art FL techniques, showing its generality.