GMBFL: Optimizing Mutation-Based Fault Localization via Graph Representation

Mutation-based fault localization has shown promising accuracy in localizing faults due to its finer analysis granularity. However, the effectiveness is limited when dealing with diverse real-world systems and actual faults, which results from its inflexible suspiciousness calculation and oversimplification of information. In this work, we propose a novel Mutation-Based Fault Localization technique, GMBFL, which utilizes Graph representation to achieve multi-information cooperation to improve fault localization. GMBFL comprises two key components: a fine-grained graph-based representation to fully utilize the information of the program, and an effective suspiciousness measure using the graph neural network to learn useful features from the graph. We evaluate GMBFL on 243 real faulty programs from Defects4J. The experimental results show that GMBFL can surpass both the state-of-the-art learning-based fault localization technique and 70 commonly used SBFL and MBFL techniques. In particular, GMBFL localizes 125 faults within TOP-1 whereas the best baseline technique can at most localize 109 faults within TOP-1.