Don’t care in SMT: building flexible yet efficient abstraction/refinement solvers

  • Andreas Bauer
  • Martin Leucker
  • Christian Schallhart
  • Michael Tautschnig
Special Section On ISoLA 2007


This paper describes a method for combining “off-the-shelf” SAT and constraint solvers for building an efficient Satisfiability Modulo Theories (SMT) solver for a wide range of theories. Our method follows the abstraction/refinement approach to simplify the implementation of custom SMT solvers. The expected performance penalty by not using an interweaved combination of SAT and theory solvers is reduced by generalising a Boolean solution of an SMT problem first via assigning don’t care to as many variables as possible. We then use the generalised solution to determine a thereby smaller constraint set to be handed over to the constraint solver for a background theory. We show that for many benchmarks and real-world problems, this optimisation results in considerably smaller and less complex constraint problems. The presented approach is particularly useful for assembling a practically viable SMT solver quickly, when neither a suitable SMT solver nor a corresponding incremental theory solver is available. We have implemented our approach in the ABsolver framework and applied the resulting solver successfully to an industrial case-study: the verification problems arising in verifying an electronic car steering control system impose non-linear arithmetic constraints, which do not fall into the domain of any other available solver.


SMT Verification Constraint solver 


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Andreas Bauer
    • 1
  • Martin Leucker
    • 2
  • Christian Schallhart
    • 3
  • Michael Tautschnig
    • 3
  1. 1.Australian National University and National ICT Australia (NICTA)CanberraAustralia
  2. 2.Institut für Informatik, Technische Universität MünchenMunichGermany
  3. 3.FB InformatikTechnische Universität DarmstadtDarmstadtGermany

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