Statistical Model Checking Meets Property-Based Testing

In recent years, statistical model checking (SMC) has become increasingly popular, because it scales well to larger stochastic models and is relatively simple to implement. SMC solves the model checking problem by simulating the model for finitely many executions and uses hypothesis testing to infer if the samples provide statistical evidence for or against a property. Being based on simulation and statistics, SMC avoids the state-space explosion problem well-known from other model checking algorithms. In this paper we show how SMC can be easily integrated into a property-based testing framework, like FsCheck for C#. As a result we obtain a very flexible testing and simulation environment, where a programmer can define models and properties in a familiar programming language. The advantages: no external modelling language is needed and both stochastic models and implementations can be checked. In addition, we have access to the powerful test-data generators of a property-based testing tool. We demonstrate the feasibility of our approach by repeating three experiments from the SMC literature.