GoRRiLA and Hard Reality

  • Konstantin Korovin
  • Andrei Voronkov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7162)


We call a theory problem a conjunction of theory literals and a theory solver any system that solves theory problems. For implementing efficient theory solvers one needs benchmark problems, and especially hard ones. Unfortunately, hard benchmarks for theory solvers are notoriously difficult to obtain. In this paper we present two tools: Hard Reality for generating theory problems from real-life problems with non-trivial boolean structure and GoRRiLA for generating random theory problems for linear arithmetic. Using GoRRiLA one can generate problems containing only a few variables, which however are difficult for all state-of-the-art solvers we tried. Such problems can be useful for debugging and evaluating solvers on small but hard problems. Using Hard Reality one can generate hard theory problems which are similar to problems found in real-life applications, for example, those taken from SMT-LIB [2].


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andrews, P.B.: Theorem proving via general matings. Journal of the ACM 28(2), 193–214 (1981)zbMATHCrossRefGoogle Scholar
  2. 2.
    Barrett, C., Ranise, S., Stump, A., Tinelli, C.: The Satisfiability Modulo Theories Library, SMT-LIB (2008),
  3. 3.
    Barrett, C., Tinelli, C.: CVC3. In: Damm, W., Hermanns, H. (eds.) CAV 2007. LNCS, vol. 4590, pp. 298–302. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Bibel, W.: On matrices with connections. Journal of the ACM 28(4), 633–645 (1981)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Brummayer, R., Biere, A.: Fuzzing and delta-debugging SMT solvers. In: 7th Intl. Workshop on on Satisfiability Modulo Theories, SMT 2009 (2009)Google Scholar
  6. 6.
    de Moura, L.M., Bjørner, N.: Z3: An Efficient SMT Solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Faure, G., Nieuwenhuis, R., Oliveras, A., Rodríguez-Carbonell, E.: SAT Modulo the Theory of Linear Arithmetic: Exact, Inexact and Commercial Solvers. In: Kleine Büning, H., Zhao, X. (eds.) SAT 2008. LNCS, vol. 4996, pp. 77–90. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Hagen, G., Zucchelli, D., Tinelli, C.: SMT parser v3.0,
  9. 9.
    Mitchell, D.G., Selman, B., Levesque, H.J.: Hard and easy distributions of SAT problems. In: AAAI 1992, pp. 459–465. AAAI Press/MIT Press (1992)Google Scholar
  10. 10.
    Nieuwenhuis, R., Hillenbrand, T., Riazanov, A., Voronkov, A.: On the Evaluation of Indexing Techniques for Theorem Proving. In: Goré, R.P., Leitsch, A., Nipkow, T. (eds.) IJCAR 2001. LNCS (LNAI), vol. 2083, pp. 257–271. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Nieuwenhuis, R., Oliveras, A.: Decision Procedures for SAT, SAT Modulo Theories and Beyond. The Barcelogic Tools. (Invited Paper). In: Sutcliffe, G., Voronkov, A. (eds.) LPAR 2005. LNCS (LNAI), vol. 3835, pp. 23–46. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Konstantin Korovin
    • 1
  • Andrei Voronkov
    • 1
  1. 1.The University of ManchesterUK

Personalised recommendations