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.

Keywords: Stochastic modeling; Model checking; State-space analysis; Markov chains; Simulation

Article Outline

1. Introduction

2. Overview of S m A r T

3. S m A r T language

3.1. S m A r T types and operators

3.2. Function declarations

3.3. Arrays

3.4. Fixed-point iterations

3.5. Random variables

3.6. Modeling formalisms

3.7. Advanced modeling features

3.8. Efficiency considerations

4. Logical solution engines

4.1. State-space generation and storage

4.2. CTL model checking

5. Stochastic solution engines

5.1. Numerical analysis of Markov and non-Markov models

5.2. Discrete-event simulation

5.3. Kronecker encoding of the Markov chain matrix

5.4. Approximations

6. Conclusions and future directions

Acknowledgements

Appendix A. S m A r T language BNF

References

Vitae

Expanded version of a paper presented at the International Conference on Modeling Techniques and Tools for Computer Performance Evaluation (TOOLS), Urbana, IL, USA, September 2003.