ML Anthology

Statistical Debugging: Simultaneous Identification of Multiple Bugs

Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken
ICML 2006 pp. 1105-1112
doi:10.1145/1143844.1143983 /icml/2006/zheng2006icml-statistical/

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.

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Cite

Text

Zheng et al. "Statistical Debugging: Simultaneous Identification of Multiple Bugs." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143983

Markdown

[Zheng et al. "Statistical Debugging: Simultaneous Identification of Multiple Bugs." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/zheng2006icml-statistical/) doi:10.1145/1143844.1143983

BibTeX

@inproceedings{zheng2006icml-statistical,
  title     = {{Statistical Debugging: Simultaneous Identification of Multiple Bugs}},
  author    = {Zheng, Alice X. and Jordan, Michael I. and Liblit, Ben and Naik, Mayur and Aiken, Alex},
  booktitle = {International Conference on Machine Learning},
  year      = {2006},
  pages     = {1105-1112},
  doi       = {10.1145/1143844.1143983},
  url       = {https://mlanthology.org/icml/2006/zheng2006icml-statistical/}
}