Reliable sequential testing for statistical model checking

We introduce a framework for comparing statistical model checking (SMC) techniques and propose a new, more reliable, SMC technique. Statistical model checking has recently been implemented in tools like UPPAAL and PRISM to be able to handle models which are too complex for numerical analysis. However, these techniques turn out to have shortcomings, most notably that the validity of their outcomes depends on parameters that must be chosen a priori. Our new technique does not have this problem; we prove its correctness, and numerically compare its performance to existing techniques.