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10. Statistical Debugging: Simultaneous Identification of Multiple Bugs
A. Zheng, M. Jordan, B. Liblit, M. Naik, A. Aiken

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

Citation formats  
  • 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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