Symbolic Compositional Verification by Learning Assumptions

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Abstract

The verification problem for a system consisting of components can be decomposed into simpler subproblems for the components using assume-guarantee reasoning. However, such compositional reasoning requires user guidance to identify appropriate assumptions for components. In this paper, we propose an automated solution for discovering assumptions based on the L * algorithm for active learning of regular languages. We present a symbolic implementation of the learning algorithm, and incorporate it in the model checker NuSMV. Our experiments demonstrate significant savings in the computational requirements of symbolic model checking.

This research was partially supported by ARO grant DAAD19-01-1-0473, and NSF grants ITR/SY 0121431 and CCR0306382.