Counterexample-Preserving Reduction for Symbolic Model Checking

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.