A probabilistic extension of UML statecharts: specification and verification

This paper is the extended technical report that corresponds to a published paper [14]. This paper introduces means to specify system randomness within UML statecharts, and to verify probabilistic temporal properties over such enhanced statecharts which we call probabilistic UML statecharts. To achieve this, we develop a general recipe to extend a statechart semantics with discrete probability distributions, resulting in Markov decision processes as semantic models. We apply this recipe to the requirements-level UML semantics of [8]. Properties of interest for probabilistic statecharts are expressed in PCTL, a probabilistic variant of CTL for processes that exhibit both non-determinism and probabilities. Verification is performed using the model checker Prism. A model checking example shows the feasibility of the suggested approach

Quantitative Evaluation of Dependability Critical Systems Based on Guarded Statechart Models

The paper introduces a method to model embedded dependability−critical systems as AND−composition of Guarded Statecharts which are special UML− statecharts. With Guarded Statecharts we can model the reactive behavior of embedded systems so that their quantitative analysis can be performed. First, we present our motivation for using Guarded Statecharts to express the interaction between hardware and software compo− nents of embedded systems, and to model faults and errors as state perturbations. Then we discuss how these models are transformed into Stochastic Reward Nets amenable to a quantitative dependability analysis. Finally, our approach is illustrated by an example

Quantitative Evaluation of Dependability Critical Systems Based on Guarded Statechart Models

The paper introduces a method to model embedded dependability−critical systems as AND−composition of Guarded Statecharts which are special UML− statecharts. With Guarded Statecharts we can model the reactive behavior of embedded systems so that their quantitative analysis can be performed. First, we present our motivation for using Guarded Statecharts to express the interaction between hardware and software compo− nents of embedded systems, and to model faults and errors as state perturbations. Then we discuss how these models are transformed into Stochastic Reward Nets amenable to a quantitative dependability analysis. Finally, our approach is illustrated by an example