Low-overhead and fully automated statistical debugging with abstraction refinement

Abstract

Cooperative statistical debugging is an effective approach for diagnosing production-run failures. To quickly identify failure predictors from the huge program predicate space, existing techniques rely on random or heuristics-guided predicate sampling at the user side. However, none of them can satisfy the requirements of low cost, low diagnosis latency, and high diagnosis quality simultaneously, which are all indispensable for statistical debugging to be practical. This paper presents a new technique that tackles the above challenges. We formulate the technique as an instance of abstraction refinement, where efficient abstract-level profiling is first applied to the whole program and its execution brings information that can pinpoint suspicious coarse-grained entities that need to be refined. The refinement profiles a corresponding set of fine-grained entities, and generates feedback that determines what to prune and what to refine next. The process is fully automated, and more importantly, guided by a mathematically rigorous analysis that guarantees that our approach produces the same debugging results as an exhaustive analysis in deterministic settings. We have implemented this technique for both C and Java on both single machine and distributed system. A thorough evaluation demonstrates that our approach yields (1) an order of magnitude reduction in the user-side runtime overhead even compared to a sampling-based approach and (2) two orders of magnitude reduction in the size of data transferred over the network, completely automatically without sacrificing any debugging capability.