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Statistical model checking for parameterized models

Benoit Delahaye Paulin Fournier 1 Didier Lime 2
1 AeLoS - Architectures et Logiciels Sûrs
LS2N - Laboratoire des Sciences du Numérique de Nantes
2 STR - STR
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : We propose a simulation-based technique, in the spirit of Statistical Model Checking, for approximate verification of probabilis-tic models with parametric transitions, and we focus in particular on parametric Markov chains. Our technique is based on an extension of Monte Carlo algorithms that allows to approximate the probability of satisfying a given finite trace property as a (polynomial) function of the parameters of the model. The confidence intervals associated with this approximation can also be expressed as a function of the parameters. In the paper, we present both the theoretical foundations of this technique and a prototype implementation in Python which we evaluate on a set of benchmarks.
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https://hal.archives-ouvertes.fr/hal-02021064
Contributor : Benoît Delahaye <>
Submitted on : Friday, February 15, 2019 - 4:02:22 PM
Last modification on : Tuesday, January 5, 2021 - 4:26:09 PM
Long-term archiving on: : Thursday, May 16, 2019 - 5:24:44 PM

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Benoit Delahaye, Paulin Fournier, Didier Lime. Statistical model checking for parameterized models. 2019. ⟨hal-02021064⟩

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