SOBER: statistical model-based bug localization

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 ...

• Type: Conference
• Year: 2005
• Where: SIGSOFT
• Authors: Chao Liu 0001, Jiawei Han, Long Fei, Samuel P. Midkiff, Xifeng Yan
• Keywords: Incorrect Runs Differs, Program Predicates, Select Predicates, SIGSOFT 2005, Software Engineering