Combining model checking and testing with an application to reliability prediction and distribution

Testing provides a probabilistic assurance of system correctness. In general, testing relies on the assumptions that the system under test is deterministic so that test cases can be sampled. However, a challenge arises when a system under test behaves non-deterministiclly in a dynamic operating environment because it will be unknown how to sample test cases. In this work, we propose a method combining hypothesis testing and probabilistic model checking so as to provide the ``assurance" and quantify the error bounds. The idea is to apply hypothesis testing to deterministic system components and use probabilistic model checking techniques to lift the results through non-determinism. Furthermore, if a requirement on the level of ``assurance" is given, we apply probabilistic model checking techniques to push down the requirement through non-determinism to individual components so that they can be verified using hypothesis testing. We motivate and demonstrate our method through an application of system reliability prediction and distribution. Our approach has been realized in a toolkit named RaPiD, which has been applied to investigate two real-world systems.