Statistical debugging: simultaneous identification of multiple bugs

Authors:
Alice X. Zheng;Michael I. Jordan;Ben Liblit;Mayur Naik;Alex Aiken
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;University of California, Berkeley, CA;University of Wisconsin-Madison, Madison, WI;Stanford University, Stanford, CA;Stanford University, Stanford, CA
Venue:
ICML '06 Proceedings of the 23rd international conference on Machine learning
Year:
2006

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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.