Symbolic Compositional Verification by Learning Assumptions

  • Rajeev Alur
  • P. Madhusudan
  • Wonhong Nam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3576)

Abstract

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 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. Our experiments demonstrate significant savings in the computational requirements of symbolic model checking.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rajeev Alur
    • 1
  • P. Madhusudan
    • 2
  • Wonhong Nam
    • 1
  1. 1.University of Pennsylvania 
  2. 2.University of Illinois at Urbana-Champaign 

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