Reducing confounding bias in predicate-level statistical debugging metrics

  • Authors:

  • Ross Gore;Paul F. Reynolds, Jr.

  • Affiliations:

  • University of Virginia, USA;University of Virginia, USA

  • Venue:
  • Proceedings of the 34th International Conference on Software Engineering
  • Year:
  • 2012

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Abstract

Statistical debuggers use data collected during test case execution to automatically identify the location of faults within software. Recent work has applied causal inference to eliminate or reduce control and data flow dependence confounding bias in statement-level statistical debuggers. The result is improved effectiveness. This is encouraging but motivates two novel questions: (1) how can causal inference be applied in predicate-level statistical debuggers and (2) what other biases can be eliminated or reduced. Here we address both questions by providing a model that eliminates or reduces control flow dependence and failure flow confounding bias within predicate-level statistical debuggers. We present empirical results demonstrating that our model significantly improves the effectiveness of a variety of predicate-level statistical debuggers, including those that eliminate or reduce only a single source of confounding bias.