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Learning-based Symbolic Assume-guarantee Reasoning with Automatic Decomposition

Summary: Learning-based Symbolic Assume-guarantee
Reasoning with Automatic Decomposition
Wonhong Nam and Rajeev Alur
Dept. of Computer and Information Science
University of Pennsylvania
{wnam, alur}@cis.upenn.edu
Abstract. Compositional reasoning aims to improve scalability of veri-
fication tools by reducing the original verification task into subproblems.
The simplification is typically based on the assume-guarantee reason-
ing principles, and requires decomposing the system into components as
well as identifying adequate environment assumptions for components.
One recent approach to automatic derivation of adequate assumptions is
based on the L
algorithm for active learning of regular languages. In this
paper, we present a fully automatic approach to compositional reasoning
by automating the decomposition step using an algorithm for hypergraph
partitioning for balanced clustering of variables. We also propose heuris-
tic improvements to the assumption identification phase. We report on
an implementation based on NuSMV, and experiments that study the
effectiveness of automatic decomposition and the overall savings in the


Source: Alur, Rajeev - Department of Computer and Information Science, University of Pennsylvania


Collections: Computer Technologies and Information Sciences