Section: New Results
Fundamental results and algorithms: statistical model checking
Participants : Sean Sedwards, Cyrille Jégourel, Axel Legay.
Our work on statistical model checking (SMC) avoids an explicit representation of the state space by building a statistical model of the executions of a system and giving results within confidence bounds. The key challenges of this approach are to reduce the length (simulation steps and cpu time) and number of simulation traces necessary to achieve a result with given confidence. Rare properties pose a particular problem in this respect, since they are not only difficult to observe but their probability is difficult to bound. A further goal is to make a tool where the choice of modelling language and logic are flexible.
We have developed the prototype of a compact, modular and efficient SMC platform which we have named PLASMA (PLatform for Statistical Model checking Algorithms). PLASMA incorporates an efficient discrete event simulation algorithm and features an importance sampling engine that can reduce the necessary number of simulation runs when properties are rare. We have found that PLASMA performs significantly better than PRISM (the de facto reference probabilistic model checker) when used in a similar mode: PLASMA's simulation algorithm scales with a lower order and can handle much larger models. When using importance sampling, PLASMA's performance with rare properties is even better.