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.
Among these, authors in REF present a statistical debugging algorithm that aims at separating the effects of different bugs and identifies predictors that are associated with individual bugs, with the goal of making it easier to prioritize debugging efforts.
Scalable statistical bug isolation
PLDI '05
Computer Science