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.