Theory and Practice, Do They Match? A Case with Spectrum-Based Fault Localization

Spectrum-based fault localization refers to the process of identifying program units that are buggy from two sets of execution traces: normal traces and faulty traces. These approaches use statistical formulas to measure the suspiciousness of program units based on the execution traces. There have been many spectrum-based fault localization approaches proposing various formulas in the literature. Two of the best performing and well-known ones are Tarantula and Ochiai. Recently, Xie et al. find that theoretically, under certain assumptions, two families of spectrum-based fault localization formulas outperform all other formulas including those of Tarantula and Ochiai. In this work, we empirically validate Xie et al.´s findings by comparing the performance of the theoretically best formulas against popular approaches on a dataset containing 199 buggy versions of 10 programs. Our empirical study finds that Ochiai and Tarantula statistically significantly outperforms 3 out of 5 theoretically best fault localization techniques. For the remaining two, Ochiai also outperforms them, albeit not statistically significantly. This happens because an assumption in Xie et al.´s work is not satisfied in many fault localization settings.