Statistical Debugging of Sampled Programs

This research was conducted by Alice X. Zheng, Michael I. Jordan, Ben Liblit, and Alex Aiken. The paper has been published in the proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003).


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 defined 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.

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