00000cab a22000003a 4500 UP-99796217608835186 Buklod 20231007234838.0 m |o d | ta 090514s xx d | ||r ||||| || DENGII eng Liu, Chao SOBER statistical model based bug localization. pp. 27 Automated localization of software bugs is one of the essential issues in debugging aids. Previous studies indicated that the evaluation history of program predicates may disclose 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 statistical 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 pattern in incorrect runs differs significantly from that in correct ones. SOBER features a principled quantification of the pattern 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 calculator on UNIX/Linux), one of which has never been reported before. Software. Software engineering. Testing and Debugging. Debugging aids. Diagnostics. Tracing. Testing tools (e.g. Data generators, coverage testing). Algorithms. Experimentation. Reliability. Verification. Localization metrics. Statistical debugging. Software engineering notes. 30, 5 (2005). FO UPD DENG-II Article