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.

PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis

“…This "whitebox" scenario assumes that programmers can monitor software internals, which often need to instrument the software to monitor execution behaviors. Typical debug approaches include software profiling [16], [17], visualization [18], and program analysis techniques like program slicing [19], delta debugging, and statistical debugging [11], [12], [13], [20], [21]. Often, the end goal is to isolate buggy program components that represent performance bottlenecks.…”

Software fault localisation: a systematic mapping study

“…The predicates will be rank based on their suspicious scores for a debugger to find the bugs. Statistical-based techniques such as Crosstab, Liblit05, and SOBER have proven to be effective in identifying the location of program bugs [28][29][30]. Slicing-based techniques extract a subset of program statements that can affect the values of interested variables at the point where a fault is manifested.…”