  Stochastic model checking is a technique for analyzing systems that possess
probabilistic characteristics. However, its scalability is limited as
probabilistic models of real-world applications typically have very large or
infinite state space. This paper presents a new infinite state CTMC model
checker, STAMINA, with improved scalability. It uses a novel state space
approximation method to reduce large and possibly infinite state CTMC models to
finite state representations that are amenable to existing stochastic model
checkers. It is integrated with a new property-guided state expansion approach
that improves the analysis accuracy. Demonstration of the tool on several
benchmark examples shows promising results in terms of analysis efficiency and
accuracy compared with a state-of-the-art CTMC model checker that deploys a
similar approximation method.
