As a key phase in software testing and debugging, fault localization can significantly influence the efficiency of fixing software faults. Among the various techniques, Mutation-Based Fault Localization (MBFL) is a widely studied fault localization technique that uses mutation analysis to guide the process of localizing faults. However, as the essential input source for MBFL, traditional mutation generates syntactical mutants, which cannot mimic the real faults and may affect the fault localization effectiveness. To address this issue, we resort to a code pre-trained model for program mutation, which is called neural mutation. Neural mutation can generate semantical mutants and even utilize the context information surrounding the mutation position. Based on the neural mutation, we propose Neural-MBFL by utilizing the high-quality mutants generated by neural mutation. To evaluate the effectiveness of Neural- MBFL, we conduct experiments on 393 faulty programs from the Defects4J benchmark. The experiment results show that Neural-MBFL can localize more faults than traditional MBFL in terms of TOP-N (i.e., 9 for TOP-I, 17 for TOP-3 and 18 for TOP-5 on average) and MAP (i.e., 2.32% relative improvement on average). We also analyze the unique faults localized by Neural-MBFL and traditional MBFL. The statistical results show their complementarity. It motivates further analysis into the repair pattern distributions between Neural-MBFL and traditional MBFL to better understand their complementarity. By further comprehensive analysis of the repair pattern distribution, traditional MBFL has advantages in localizing faults related to rule-based code modifications. In contrast, Neural-MBFL has advantages in localizing complex faults requiring deep code comprehension. These findings show that incorporating neural mutation is promising in improving the effectiveness of MBFL.

Abstract Fault localization is a complex, costly and time‐consuming task in software debugging. Numerous automated techniques have been developed to expedite this process. Mutation‐based fault localization (MBFL) is one of the most widely studied techniques which uses mutation analysis to generate mutants for revealing potential faults in the program. However, our theoretical analysis exposes an inherent conflict between the fundamental assumption and the essential meaning of existing MBFL suspiciousness. This conflict is caused by mutant bias. Intuitively, the suspiciousness can be corrected by eliminating the mutant bias for more accurately measuring the faulty probability of the corresponding mutant statement. In this paper, we introduce Delta4Ms, a fault localization approach designed to eliminate mutant bias. Delta4Ms integrates the principles of signal theory, modelling the actual suspiciousness and mutant bias as the desired and false signal components, respectively. Based on theoretical derivation, the average suspiciousness of mutants serves as an estimate of mutant bias. Delta4Ms effectively mitigates mutant bias, extracting the desired signal and yielding corrected suspiciousness for fault localization. To precisely estimate mutant bias, higher order mutants (HOMs) are incorporated. We conduct an extensive experimental evaluation of Delta4Ms on 320 real‐fault programs from Codeflaws. The results indicate that our model significantly outperforms existing SBFL and MBFL techniques, showing a considerable improvement in fault localization effectiveness. We further assessed the robustness of Delta4Ms by examining different HOM ratios and HOM generation strategies. Moreover, Delta4Ms achieves a substantial reduction in mutation execution cost and minimal accuracy loss through the implementation of test case reduction. Finally, we perform preliminary experiments on 15 real‐fault programs from the Defects4J benchmark to assess the generalization of the model's fault localization effectiveness.