Effective Multi-Fault Localization Based on Fault-Relevant Statistics

Fault localization can facilitate software debugging and thus is a key technology in software maintenance. Spectrum-based fault localization (SBFL) has been known as an effective and lightweight approach. Nevertheless, the existence of multiple faults within the same system is still the bottleneck of SBFL. Inspired by feature selection in data mining, this paper improves the Relief algorithm and proposes fault-relevant statistics (FRS) to calculate the suspiciousness of program elements. Specifically, it takes test cases as samples, execution results as labels, and spectrum information as features, so that the problem of multi-fault localization can be viewed as a feature selection problem. Unlike the clustering method, this paper only takes into account the closest failing and successful case for each sampled test case when computing FRS. Experiments on open source software systems show that FRS improves the efficiency of fault localization with low computational cost.