Sober: statistical model-based bug localization

Automated localization of software bugs is one of the es-sential issues in debugging aids. Previous studies indicated that the evaluation history of program predicates may dis-close important clues about underlying bugs. In this paper, we propose a new statistical model-based approach, called SOBER, which localizes software bugs without any prior knowledge of program semantics. Unlike existing statisti-cal debugging approaches that select predicates correlated with program failures, SOBER models evaluation patterns of predicates in both correct and incorrect runs respectively and regards a predicate as bug-relevant if its evaluation pat-tern in incorrect runs differs significantly from that in correct ones. SOBER features a principled quantification of the pat-tern difference that measures the bug-relevance of program predicates. We systematically evaluated our approach under the same setting as previous studies. The result demonstrated the power of our approach in bug localization: SOBER can help programmers locate 68 out of 130 bugs in the Siemens suite when programmers are expected to examine no more than 10 % of the code, whereas the best previously reported is 52 out of 130. Moreover, with the assistance of SOBER, we found two bugs in bc 1.06 (an arbitrary precision calcula-tor on UNIX/Linux), one of which has never been reported before