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Manual debugging is tedious, as well as costly. The high cost has motivated the development of fault localization techniques, which help developers search for fault locations. In this paper, we propose a new statistical method, called SOBER, which automatically localizes software faults without any prior knowledge of the program semantics. Unlike existing statistical approaches that select predicates correlated with program failures, SOBER models the predicate evaluation in both correct and incorrect executions and regards a predicate as fault-relevant if its evaluation pattern in incorrect executions significantly diverges from that in correct ones. Featuring a rationale similar to that of hypothesis testing, SOBER quantifies the fault relevance of each predicate in a principled way. We systematically evaluate SOBER under the same setting as previous studies. The result clearly demonstrates the effectiveness: SOBER …