QUASY: Quantitative Synthesis Tool

  • Krishnendu Chatterjee
  • Thomas A. Henzinger
  • Barbara Jobstmann
  • Rohit Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6605)


We present the tool Quasy, a quantitative synthesis tool. Quasy takes qualitative and quantitative specifications and automatically constructs a system that satisfies the qualitative specification and optimizes the quantitative specification, if such a system exists. The user can choose between a system that satisfies and optimizes the specifications (a) under all possible environment behaviors or (b) under the most-likely environment behaviors given as a probability distribution on the possible input sequences. Quasy solves these two quantitative synthesis problems by reduction to instances of 2-player games and Markov Decision Processes (MDPs) with quantitative winning objectives. Quasy can also be seen as a game solver for quantitative games. Most notable, it can solve lexicographic mean-payoff games with 2 players, MDPs with mean-payoff objectives, and ergodic MDPs with mean-payoff parity objectives.


  1. 1.
    Baier, C., Haverkort, B.R., Hermanns, H., Katoen, J.-P.: Performance evaluation and model checking join forces. Commun. ACM 53(9) (2010)Google Scholar
  2. 2.
    Behrmann, G., Bengtsson, J., David, A., Larsen, K.G., Pettersson, P., Yi, W.: Uppaal implementation secrets. In: Formal Techniques in Real-Time and Fault Tolerant Systems (2002)Google Scholar
  3. 3.
    Bloem, R., Chatterjee, K., Henzinger, T.A., Jobstmann, B.: Better quality in synthesis through quantitative objectives. In: Bouajjani, A., Maler, O. (eds.) CAV 2009. LNCS, vol. 5643, pp. 140–156. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Bloem, R., Greimel, K., Henzinger, T.A., Jobstmann, B.: Synthesizing robust systems. In: FMCAD (2009)Google Scholar
  5. 5.
    Brim, L., Chaloupka, J.: Using strategy improvement to stay alive. CoRR, 1006.1405 (2010)Google Scholar
  6. 6.
    Černý, P., Chatterjee, K., Henzinger, T., Radhakrishna, A., Singh, R.: Quantitative synthesis for concurrent programs. Technical Report IST-2010-0004, IST Austria (2010)Google Scholar
  7. 7.
    Chatterjee, K., Henzinger, T.A., Jobstmann, B., Singh, R.: Measuring and synthesizing systems in probabilistic environments. In: Touili, T., Cook, B., Jackson, P. (eds.) CAV 2010. LNCS, vol. 6174, pp. 380–395. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Feinberg, E.A., Shwartz, A.: Handbook of Markov Decision Processes: Methods and Applications. Springer, Heidelberg (2001)MATHGoogle Scholar
  9. 9.
    Hinton, A., Kwiatkowska, M., Norman, G., Parker, D.: PRISM: A tool for automatic verification of probabilistic systems. In: Hermanns, H. (ed.) TACAS 2006. LNCS, vol. 3920, pp. 441–444. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    The Scala programming language, http://www.scala-lang.org/
  11. 11.
    Wimmer, R., Braitling, B., Becker, B., Hahn, E.M., Crouzen, P., Hermanns, H., Dhama, A., Theel, O.: Symblicit calculation of long-run averages for concurrent probabilistic systems. In: QEST (2010)Google Scholar
  12. 12.
    Zwick, U., Paterson, M.: The complexity of mean payoff games on graphs. Theor. Comput. Sci. 158(1-2), 343–359 (1996)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Krishnendu Chatterjee
    • 1
  • Thomas A. Henzinger
    • 1
    • 2
  • Barbara Jobstmann
    • 3
  • Rohit Singh
    • 4
  1. 1.Institute of Science and Technology AustriaAustria
  2. 2.École Polytechnique Fédéral de LausanneSwitzerland
  3. 3.CNRS/VerimagFrance
  4. 4.Indian Institute of Technology BombayIndia

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