Statistical Debugging of Sampled Programs

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Alice Zheng, Michael Jordan, Ben Liblit, Alex Aiken


We present a novel strategy for automatically debugging programs given sampled data from thousands of actual user runs. Our goal is to pinpoint those features that are most correlated with crashes. This is accomplished by maximizing an appropriately deļ¬ned utility function. It has analogies with intuitive debugging heuristics, and, as we demonstrate, is able to deal with various types of bugs that occur in real programs.