Scalable statistical bug isolation

TLDR

Earlier statistical algorithms that focus solely on identifying predictors that correlate with program failure perform poorly when there are multiple bugs. We present a statistical debugging algorithm that isolates bugs in programs containing multiple undiagnosed bugs. Our new technique separates the effects of different bugs and identifies predictors associated with individual bugs. The algorithm’s predictors reveal the circumstances and frequencies of bug occurrences, facilitating debugging prioritization, and case studies demonstrate it uncovered previously unknown, significant crashing bugs in widely used systems.

Abstract

We present a statistical debugging algorithm that isolates bugs in programs containing multiple undiagnosed bugs. Earlier statistical algorithms that focus solely on identifying predictors that correlate with program failure perform poorly when there are multiple bugs. Our new technique separates the effects of different bugs and identifies predictors that are associated with individual bugs. These predictors reveal both the circumstances under which bugs occur as well as the frequencies of failure modes, making it easier to prioritize debugging efforts. Our algorithm is validated using several case studies, including examples in which the algorithm identified previously unknown, significant crashing bugs in widely used systems.