Statistical debugging: simultaneous identification of multiple bugs

Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken

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.