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Abstraction Refinement for Bounded Model Checking

  • Anubhav Gupta
  • Ofer Strichman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3576)

Abstract

Counterexample-Guided Abstraction Refinement (cegar) techniques have been very successful in model checking large systems. While most previous work has focused on model checking, this paper presents a Counterexample-Guided abstraction refinement technique for Bounded Model Checking (bmc). Our technique makes bmc much faster, as indicated by our experiments. bmc is also used for generating refinements in the Proof-Based Refinement (pbr) framework. We show that our technique unifies pbr and cegar into an abstraction-refinement framework that can balance the model checking and refinement efforts.

Keywords

Model Check Abstract Model Choice Function Concrete Model Propositional Formula 
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 2005

Authors and Affiliations

  • Anubhav Gupta
    • 1
  • Ofer Strichman
    • 2
  1. 1.School of Computer ScienceCarnegie Mellon UniversityUSA
  2. 2.Information Systems EngineeringTechnionIsrael

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