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Counterexample-Preserving Reduction for Symbolic Model Checking

机译:符号模型检查的反例抑制减少

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摘要

The cost of LTL model checking is highly sensitive to the length of the formula under verification. We observe that, under some specific conditions, the input LTL formula can be reduced to an easier-to-handle one before model checking. In our reduction, these two formulae need not to be logically equivalent, but they share the same counterexample set w.r.t the model. In the case that the model is symbolically represented, the condition enabling such reduction can be detected with a lightweight effort (e.g., with SAT-solving). In this paper, we tentatively name such technique "CounterExample-Preserving REduction" (CEPRE, for short), and the proposed technique is experimentally evaluated by adapting NuSMV.
机译:LTL模型检查的成本对验证下公式的长度非常敏感。我们观察到,在一些特定条件下,可以在模型检查之前将输入的LTL公式变为更容易处理的公式。在减少时,这两个公式不需要逻辑等同于,但它们共享相同的反例设置W.r.t。在象征性地表示模型的情况下,可以用轻量级努力(例如,通过SAT求解)来检测使得这种降低的条件。在本文中,我们暂时命名了这样的技术“反例保留了减少”(CEPRE,短暂的),并通过适应NUSMV来实验评估所提出的技术。

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