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Towards Automated Strategies in Satisfiability Modulo Theory

  • Nicolás Gálvez RamírezEmail author
  • Youssef Hamadi
  • Eric Monfroy
  • Frédéric Saubion
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9594)

Abstract

SMT solvers include many heuristic components in order to ease the theorem proving process for different logics and problems. Handling these heuristics is a non-trivial task requiring specific knowledge of many theories that even a SMT solver developer may be unaware of. This is the first barrier to break in order to allow end-users to control heuristics aspects of any SMT solver and to successfully build a strategy for their own purposes. We present a first attempt for generating an automatic selection of heuristics in order to improve SMT solver efficiency and to allow end-users to take better advantage of solvers when unknown problems are faced. Evidence of improvement is shown and the basis for future works with evolutionary and/or learning-based algorithms are raised.

Keywords

SMT Strategy Z3 Learning Tuning Evolutionary algorithm Search-based software engineering 

Notes

Acknowledgment

We want to thanks Christopher Wintersteiger from Microsoft Research for provide us critical information about Z3 theorem prover. Nicolás Gálvez Ramírez is granted by Chilean government: CONICYT-PCHA / Doctorado Nacional / 2013-21130089.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Nicolás Gálvez Ramírez
    • 1
    • 2
    Email author
  • Youssef Hamadi
    • 4
  • Eric Monfroy
    • 3
  • Frédéric Saubion
    • 2
  1. 1.LabDIIUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.LERIAUniversité d’AngersAngersFrance
  3. 3.LINA, UMR CNRS 6241, TASC INRIA, Université de NantesNantesFrance
  4. 4.LIXEcole PolytechniquePalaiseauFrance

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