Symbolic Compositional Verification by Learning Assumptions

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摘要：

Compositional reasoning aims to improve scalability of verification tools by reducing the original verification task into subproblems. The simplification is typically based on assume-guarantee reasoning principles, and requires user guidance to identify appropriate assumptions for components. In this paper, we propose a fully automated approach to compositional reasoning that consists of automated decomposition using a hypergraph partitioning algorithm for balanced clustering of variables, and discovering assumptions using 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 . In some cases, our experiments demonstrate significant savings in the computational requirements of symbolic model checking.

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关键词：

formal verification symbolic model checking compositional verification assume-guarantee reasoning regular language learning hypergraph partitioning

DOI：

10.1007/s10703-008-0055-8

被引量：

232

年份：

2008