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Citation
A. Zheng, M. Jordan, B. Liblit, M. Naik, A. Aiken. "10. Statistical Debugging: Simultaneous Identification of Multiple Bugs". International Conference on Machine Learning, 1105-1112, June, 2006.

Abstract
We describe a statistical approach to software debugging in the presence of multiple bugs. Due to sparse sampling issues and complex interaction between program predicates, many generic off-the-shelf algorithms fail to select useful bug predictors. Taking inspiration from bi-clustering algorithms, we propose an iterative collective voting scheme for the program runs and predicates. We demonstrate successful debugging results on several real world programs and a large debugging benchmark suite.

Electronic downloads

• http://theory.stanford.edu/~aiken/publications/papers/icml2006.pdf

• HTML

  A. Zheng, M. Jordan, B. Liblit, M. Naik, A. Aiken. <a
  href="http://www.truststc.org/pubs/615.html"
  >10.	Statistical Debugging: Simultaneous Identification
  of Multiple Bugs</a>, International Conference on
  Machine Learning, 1105-1112, June, 2006.

• Plain text

  A. Zheng, M. Jordan, B. Liblit, M. Naik, A. Aiken.
  "10.	Statistical Debugging: Simultaneous Identification
  of Multiple Bugs". International Conference on Machine
  Learning, 1105-1112, June, 2006.

• BibTeX

  @inproceedings{ZhengJordanLiblitNaikAiken06_10StatisticalDebuggingSimultaneousIdentificationOf,
      author = {A. Zheng and M. Jordan and B. Liblit and M. Naik
                and A. Aiken},
      title = {10.	Statistical Debugging: Simultaneous
                Identification of Multiple Bugs},
      booktitle = {International Conference on Machine Learning},
      pages = {1105-1112},
      month = {June},
      year = {2006},
      abstract = {We describe a statistical approach to software
                debugging in the presence of multiple bugs. Due to
                sparse sampling issues and complex interaction
                between program predicates, many generic
                off-the-shelf algorithms fail to select useful bug
                predictors. Taking inspiration from bi-clustering
                algorithms, we propose an iterative collective
                voting scheme for the program runs and predicates.
                We demonstrate successful debugging results on
                several real world programs and a large debugging
                benchmark suite.},
      URL = {http://www.truststc.org/pubs/615.html}
  }
  

Posted by Jessica Gamble on 18 Mar 2009.
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