On Statistical Model Checking of Stochastic Systems

Statistical methods to model check stochastic systems have been, thus far, developed only for a sublogic of continuous stochastic logic (CSL) that does not have steady state operators and unbounded until formulas. In this paper, we present a statistical model checking algorithm that also verifies CSL formulas with unbounded untils. The algorithm is based on Monte Carlo simulation of the model and hypothesis testing of the samples, as opposed to sequential hypothesis testing. The use of statistical hypothesis testing allows us to exploit the inherent parallelism in this approach. We have implemented the algorithm in a tool called VESTA, and found it to be effective in verifying several examples