Model-Based Debugging with High-Level Observations

Recent years have seen considerable developments in modeling techniques for automatic fault location in programs. However, much of this research considered the models from a standalone perspective. Instead, this paper focuses on the highly unusual properties of the testing and measurement process, where capabilities differ strongly from the classical hardware diagnosis paradigm. In particular, in an interactive debugging process user interaction may result in highly complex input to improve the process. This work extends the standard entropy-based measurement selection algorithm proposed in (de Kleer and Williams, 1987) to deal with high-level observations about the intended behavior of Java programs, specific to a set of test cases. We show how to incorporate the approach into previously developed model-based debugging frameworks and to how reasoning about high-level properties of programs can improve diagnostic results.

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