A Platform for High Performance Statistical Model Checking – PLASMA

  • Cyrille Jegourel
  • Axel Legay
  • Sean Sedwards
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7214)

Abstract

Statistical model checking offers the potential to decide and quantify dynamical properties of models with intractably large state space, opening up the possibility to verify the performance of complex real-world systems. Rare properties and long simulations pose a challenge to this approach, so here we present a fast and compact statistical model checking platform, PLASMA, that incorporates an efficient simulation engine and uses importance sampling to reduce the number and length of simulations when properties are rare. For increased flexibility and efficiency PLASMA compiles both model and property into bytecode that is executed on an in-built memory-efficient virtual machine.

Keywords

Virtual Machine Importance Sampling Simulation Trace Intermediate Language Statistical 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.

References

  1. 1.
    Gibson, M., Bruck, J.: Efficient exact stochastic simulation of chemical systems with many species and many channels. J. of Physical Chemistry A 104, 1876 (2000)CrossRefGoogle Scholar
  2. 2.
    Heidelberger, P.: Fast simulation of rare events in queueing and reliability models. ACM Trans. Model. Comput. Simul. 5, 43–85 (1995)MATHCrossRefGoogle Scholar
  3. 3.
  4. 4.
    Pnueli, A., Zuck, L.: Verification of multiprocess probabilistic protocols. Distributed Computing 1, 53–72 (1986)MATHCrossRefGoogle Scholar
  5. 5.
    Ridder, A.: Importance sampling simulations of markovian reliability systems using cross-entropy. Annals of Operations Research 134, 119–136 (2005)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Sedwards, S.: A Natural Computation Approach To Biology: Modelling Cellular Processes and Populations of Cells With Stochastic Models of P Systems. PhD thesis, University of Trento (2009)Google Scholar
  7. 7.
    Sen, K., Viswanathan, M., Agha, G.A.: Vesta: A statistical model-checker and analyzer for probabilistic systems. In: QEST, pp. 251–252. IEEE (2005)Google Scholar
  8. 8.
    Daniel, T., Gillespie: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics 22(4), 403–434 (1976)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Vilar, M.G., Kueh, H.Y., Barkai, N., Leibler, S.: Mechanisms of noise-resistance in genetic oscillators. Proceedings of the National Academy of Science 99 (2002)Google Scholar
  10. 10.
    Younes, H.L.S.: Ymer: A Statistical Model Checker. In: Etessami, K., Rajamani, S.K. (eds.) CAV 2005. LNCS, vol. 3576, pp. 429–433. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cyrille Jegourel
    • 1
  • Axel Legay
    • 1
  • Sean Sedwards
    • 1
  1. 1.INRIA Rennes – Bretagne AtlantiqueFrance

Personalised recommendations