Automated Assume-Guarantee Reasoning through Implicit Learning

We propose a purely implicit solution to the contextual assumption generation problem in assume-guarantee reasoning Instead of improving the L* algorithm — a learning algorithm for finite automata, our algorithm computes implicit representations of contextual assumptions by the CDNF algorithm — a learning algorithm for Boolean functions We report three parametrized test cases where our solution outperforms the monolithic interpolation-based Model Checking algorithm.

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