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PLDI
2005
ACM

Scalable statistical bug isolation

9 years 7 months ago
Scalable statistical bug isolation
We present a statistical debugging algorithm that isolates 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, including examples in which the algorithm identified previously unknown, significant crashing bugs in widely used systems. Categories and Subject Descriptors D.2.4 [Software Engineering]: Software/Program Verification—statistical methods; D.2.5 [Software Engineering]: Testing and Debugging—debugging aids, distributed debugging, monitors, tracing; I.5.2 [Pattern Recognition...
Ben Liblit, Mayur Naik, Alice X. Zheng, Alexander
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where PLDI
Authors Ben Liblit, Mayur Naik, Alice X. Zheng, Alexander Aiken, Michael I. Jordan
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