Localizing Software Faults Simultaneously

Current automatic diagnosis techniques are predominantly of a statistical nature and, despite typical defect densities, do not explicitly consider multiple faults, as also demonstrated by the popularity of the single-fault Siemens set. We present a logic reasoning approach, called Zoltar-M(ultiple fault), that yields multiple-fault diagnoses, ranked in order of their probability. Although application of Zoltar-M to programs with many faults requires further research into heuristics to reduce computational complexity, theory as well as experiments on synthetic program models and two multiple-fault program versions from the Siemens set show that for multiple-fault programs this approach can outperform statistical techniques, notably spectrum-based fault localization (SFL). As a side-effect of this research, we present a new SFL variant, called Zoltar-S(ingle fault), that is provably optimal for single-fault programs, outperforming all other variants known to date.