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A Parametric Interpolation Framework for First-Order Theories

  • Laura Kovács
  • Simone Fulvio Rollini
  • Natasha Sharygina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8265)

Abstract

Craig interpolation is successfully used in both hardware and software model checking. Generating good interpolants, and hence automatically determining the quality of interpolants is however a very hard problem, requiring non-trivial reasoning in first-order theories. An important class of state-of-the-art interpolation algorithms is based on recursive procedures that generate interpolants from refutations of unsatisfiable conjunctions of formulas. We analyze this type of algorithms and develop a theoretical framework, called a parametric interpolation framework, for arbitrary first-order theories and inference systems. As interpolation-based verification approaches depend on the quality of interpolants, our method can be used to derive interpolants of different structure and strength, with or without quantifiers, from the same proof. We show that some well-known interpolation algorithms are instantiations of our framework.

Keywords

Model Check Inference System Interpolation Algorithm Interpolation Procedure Bound Model Check 
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 2013

Authors and Affiliations

  • Laura Kovács
    • 1
  • Simone Fulvio Rollini
    • 2
  • Natasha Sharygina
    • 2
  1. 1.Chalmers University of TechnologySweden
  2. 2.USISwitzerland

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