Scalable Statistical Bug Isolation

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Ben Liblit, Mayur Naik, Alice X. Zheng, Alex Aiken, and Michael I. Jordan

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We present a statistical debugging algorithm that is able to isolate bugs in programs containing multiple undiagnosed bugs. Earlier statistical algorithms that focus solely on identifying predictors that correlate with program failure perform poorly when there are multiple bugs. Our new technique separates the effects of different bugs and identifies predictors that are associated with individual bugs. These predictors reveal both the circumstances under which bugs occur as well as the frequencies of failure modes, making it easier to prioritize debugging efforts. Our algorithm is validated using several case studies. These case studies include examples in which the algorithm found previously unknown, significant crashing bugs in widely used systems.

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In Proceedings of the ACM SIGPLAN 2005 Conference on Programming Language Design and Implementation (PLDI'05), pages 15-26, Chicago, Illinois, USA, June 2005.

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