Abstract: Statistical debugging is a combination of statistical machine learning and software debugging. Given sampled run-time profiles from both successful and failed runs, our task is to select a small set of program predicates that can succinctly capture the failure modes, thereby leading to the locations of the bugs. Given the diverse nature of software bugs and coding structure, this is not a trivial task. We start by assuming that there is only one bug in the program. This allows us to concentrate on the problem of non-deterministic bugs. We design a utility function whose components may be adjusted based on the suspected level of determinism of the bug. The algorithm proves to work well on two real world programs. The problems becomes much more complicated once we do away with the single-bug assumption. The original single-bug algorithm does not perform well in the presence of multiple bugs. Our initial attempts at clustering fall short of an effective solution. After identifying the main problems in the multi-bug case, we present an iterative predicate scoring algorithm. We demonstrate the algorithm at work on five real world programs, where it successfully clusters runs and identifies important predicates that clearly point to many of the underlying bugs.