This research was conducted by Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, and Alex Aiken. The paper has been published in the 23rd International Conference on Machine Learning (ICML 2006).
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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