Automated Assume-Guarantee Reasoning for Simulation Conformance

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We address the issue of efficiently automating assume-guarantee reasoning for simulation conformance between finite state systems and specifications. We focus on a non-circular assume-guarantee proof rule, and show that there is a weakest assumption that can be represented canonically by a deterministic tree automata (DTA). We then present an algorithm L T that learns this DTA automatically in an incremental fashion, in time that is polynomial in the number of states in the equivalent minimal DTA. The algorithm assumes a teacher that can answer membership and candidate queries pertaining to the language of the unknown DTA. We show how the teacher can be implemented using a model checker. We have implemented this framework in the COMFORT toolkit and we report encouraging results (over an order of magnitude improvement in memory consumption) on non-trivial benchmarks.