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PROPhESY: A PRObabilistic ParamEter SYnthesis Tool

  • Christian Dehnert
  • Sebastian Junges
  • Nils Jansen
  • Florian Corzilius
  • Matthias Volk
  • Harold Bruintjes
  • Joost-Pieter Katoen
  • Erika Ábrahám
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9206)

Abstract

We present PROPhESY, a tool for analyzing parametric Markov chains (MCs). It can compute a rational function (i.e., a fraction of two polynomials in the model parameters) for reachability and expected reward objectives. Our tool outperforms state-of-the-art tools and supports the novel feature of conditional probabilities. PROPhESY supports incremental automatic parameter synthesis (using SMT techniques) to determine “safe” and “unsafe” regions of the parameter space. All values in these regions give rise to instantiated MCs satisfying or violating the (conditional) probability or expected reward objective. PROPhESY features a web front-end supporting visualization and user-guided parameter synthesis. Experimental results show that PROPhESY scales to MCs with millions of states and several parameters.

Notes

Acknowledgements

We want to thank Ernst Moritz Hahn for valuable discussions on computing conditional probabilities for parametric MCs.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christian Dehnert
    • 1
  • Sebastian Junges
    • 1
  • Nils Jansen
    • 1
  • Florian Corzilius
    • 1
  • Matthias Volk
    • 1
  • Harold Bruintjes
    • 1
  • Joost-Pieter Katoen
    • 1
  • Erika Ábrahám
    • 1
  1. 1.RWTH Aachen UniversityAachenGermany

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