Scalable statistical bug isolation

  • Authors:
  • Ben Liblit;Mayur Naik;Alice X. Zheng;Alex Aiken;Michael I. Jordan

  • Affiliations:
  • University of Wisconsin-Madison;Stanford University;University of California, Berkeley;Stanford University;University of California, Berkeley

  • Venue:
  • Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
  • Year:
  • 2005

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Abstract

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.