Catálogo de publicaciones - libros

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 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 CFRT toolkit and we report encouraging results (over an order of magnitude improvement in memory consumption) on non-trivial benchmarks.

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 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.