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.

Search-based Test Case Generation (TCG) for web applications suffers from unstable performance and suboptimal test suite problems due to diversity loss. However, previous diversity metrics mainly only focus on client-side models or server-side code, which are prone to low robustness and poor generalization in practical applications. We propose a diversity-driven TCG method DTester, which can maximize behavior exploration and minimize the test suite size while covering more server-side vulnerable paths. Three diversity metrics (i.e. phenotypic coupling, intent coupling and competitiveness) are proposed to measure the underlying relationship between test cases from user behavior, code logic and test execution history. Moreover, a 3-dimensional weight graph is designed to model association among metrics, which provides fine-grained guidance for the genetic algorithm to generate diverse test cases from the client-side behavior model. Our empirical evaluation on five web applications shows that DTester can efficiently and robustly generate better test suites than the state-of-the-art TCG method. The maximum improvement is [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] in efficiency, test suite size, diversity and robustness.

Fault Localization (FL) is one of the most essential and time-consuming steps during software debugging. Mutation-based fault localization (MBFL) is one FL technique that has demonstrated promising fault localization accuracy in recent years. Current MBFL techniques mainly use First-Order Mutant (FOM) to localize faults, and only perform well in simple fault localization. When facing complex fault localization, MBFL with FOMs can only achieve low FL accuracy. Moreover, previous Higher-Order Mutant (HOM) generation techniques only use simple combinations of FOMs but do not consider the correlation between simple faults in the composition of complex faults. In this study, we consider the relationships between single faults and propose SFClu, a novel HOM generation method. Specifically, SFClu aims to generate HOMs to simulate complex faults consisting of multiple unrelated simple faults on multiple lines. To evaluate the performance of our proposed methods, we conduct empirical studies on 237 complex-fault programs from two datasets. The experimental results show that SFClu significantly outperforms traditional HOM generation methods (i.e., Last2First, DifferentOperators, and RandomMix). Furthermore, the experimental results also demonstrate that Higher-Order MBFL(HMBFL) with SFClu can outperform the state-of-the-art SBFL and MBFL techniques in terms of EXAM, TOP-N, and MAP metrics.