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SAT-based compositional verification using lazy learning

Published: 03 July 2007 Publication History

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.

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cover image Guide Proceedings
CAV'07: Proceedings of the 19th international conference on Computer aided verification
July 2007
562 pages
ISBN:9783540733676
  • Editors:
  • Werner Damm,
  • Holger Hermanns

Sponsors

  • German Science Foundation
  • Artist2 Network of Excellence
  • Cadence Design Systems
  • Informatik Saarland
  • IBM: IBM

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 03 July 2007

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