Section: New Results

Rare Event Analysis for Markovian Systems

Model checking real time properties on probabilistic systems requires computing transient probabilities on continuous time Markov chains. Beyond numerical analysis ability, a probabilistic framing can only be obtained using simulation. This statistical approach fails when directly applied to the estimation of very small probabilities. In [60] , combining the uniformization technique and extending our previous results, we design a method which applies to continuous time Markov chains and formulas of a timed temporal logic. The corresponding algorithm has been implemented in our tool cosmos. We present experimentations on a relevant system, with drastic time reductions with respect to standard statistical model checking.

Statistical model-checking is an alternative verification technique applied on stochastic systems whose size is beyond numerical analysis ability. Given a model (most often a Markov chain) and a formula, it provides a confidence interval for the probability that the model satisfies the formula. One of the main limitations of the statistical approach is the computation time explosion triggered by the evaluation of very small probabilities. In order to solve this problem, we develop in [59] a new approach based on importance sampling and coupling. The corresponding algorithms have been implemented in our tool cosmos. We present experimentation on several relevant systems, with estimated time reductions reaching a factor of ${10}^{1}20$.