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SAT-Based Compositional Verification Using Lazy Learning

  • Nishant Sinha
  • Edmund Clarke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4590)

Abstract

A recent approach to automated assume-guarantee reasoning (AGR) for concurrent systems relies on computing environment assumptions for components using the L * algorithm for learning regular languages. While this approach has been investigated extensively for message passing systems, it still remains a challenge to scale the technique to large shared memory systems, mainly because the assumptions have an exponential communication alphabet size. In this paper, we propose a SAT-based methodology that employs both induction and interpolation to implement automated AGR for shared memory systems. The method is based on a new lazy approach to assumption learning, which avoids an explicit enumeration of the exponential alphabet set during learning by using symbolic alphabet clustering and iterative counterexample-driven localized partitioning. Preliminary experimental results on benchmarks in Verilog and SMV are encouraging and show that the approach scales well in practice.

Keywords

Model Check Regular Language Bound Model Check Membership Query Shared Memory System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Nishant Sinha
    • 1
  • Edmund Clarke
    • 1
  1. 1.Carnegie Mellon UniversityUSA

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