Fault Localization with Non-parametric Program Behavior Model

Fault localization is a major activity in software debugging. Many existing statistical fault localization techniques compare feature spectra of successful andfailed runs. Some approaches, such as SOBER, test the similarity of the feature spectra through parametric self-proposed hypothesis testing models. Our finding shows, however, that the assumption on feature spectraforming known distributions is not well-supported by empirical data. Instead, having a simple, robust, and explanatory model is an essential move toward establishing a debugging theory. This paper proposesa non-parametric approach to measuring the similarity of the feature spectra of successful and failed runs, and picks a general hypothesis testing model, namelythe Mann-Whitney test, as the core. The empirical results on the Siemens suite show that our technique can outperform existing predicate-based statistical fault localization techniques in locating faulty statements