Self-adaptive fault localization algorithm based on predicate execution information analysis

Finding the location of a fault in code is an important research and practical problem, which often requires much time and manual effort. Predicate-based statistical fault localization (PBSFL) is a promising method, which obtains the correlative relationship between predicates and faults by comparing the predicate execution information in both correct and incorrect runs. However, experiment results show that existing PBSFL methods fail to locate some faults because they use predicate execution information in a fixed intensity, which may cause insufficient or excessive usage. To solve the problem, we propose a new method, called self-adaptive fault localization algorithm based on predicate execution information analysis, which dynamically select the intensity of information utilization for each predicate through the analysis of test cases run. Experimental results demonstrate that our approach performs well in both accuracy and stability for localizing faults in subject programs of the Siemens and space suites.