Logic and stochastic modeling with S m A r T

Abstract

We describe the main features of S m A r T, a software package providing a seamless environment for the logic and probabilistic analysis of complex systems. S m A r T can combine different formalisms in the same modeling study. For the analysis of logical behavior, both explicit and symbolic state-space generation techniques, as well as symbolic CTL model-checking algorithms, are available. For the study of stochastic and timing behavior, both sparse-storage and Kronecker-based numerical solution approaches are available when the underlying process is a Markov chain, while discrete-event simulation is always applicable regardless of the stochastic nature of the process, and certain classes of non-Markov models can also be solved numerically. Finally, since S m A r T targets both the classroom and realistic industrial settings as a learning, research, and application tool, it is written in a modular way that allows for easy integration of new formalisms and solution algorithms.