David N. Jansen

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.

The ins and outs of the probabilistic model checker MRMC

The Markov Reward Model Checker (MRMC) is a software toolfor verifying properties over probabilistic models. It supports PCTL and CSL model checking, and their rewardextensions. Distinguishing features of MRMC are its support for computing time- and reward-bounded reachability probabilities, (property-driven) bisimulation minimization, and precise on-the-fly steady-state detection. Recent tool features include time-bounded reachability analysis for uniform CTMDPs and CSL model checking by discrete-event simulation. This paper presents the tools current status and its implementation details.

Bisimulation minimisation mostly speeds up probabilistic model checking

This paper studies the effect of bisimulation minimisation in model checking of monolithic discrete-time and continuous-time Markov chains as well as variants thereof with rewards. Our results show that--as for traditional model checking--enormous state space reductions (up to logarithmic savings) may be obtained. In contrast to traditional model checking, in many cases, the verification time of the original Markov chain exceeds the quotienting time plus the verification time of the quotient. We consider probabilistic bisimulation as well as versions thereof that are tailored to the property be checked.

The Ins and Outs of the Probabilistic Model Checker MRMC

The Markov Reward Model Checker (MRMC) is a software toolfor verifying properties over probabilistic models. It supports PCTL and CSL model checking, and their rewardextensions. Distinguishing features of MRMC are its support for computing time- and reward-bounded reachability probabilities, (property-driven) bisimulation minimization, and precise on-the-fly steady-state detection. Recent tool features include time-bounded reachability analysis for uniform CTMDPs and CSL model checking by discrete-event simulation. This paper presents the tools current status and its implementation details.

How fast and fat is your probabilistic model checker? an experimental performance comparison

This paper studies the efficiency of several probabilistic model checkers by comparing verification times and peak memory usage for a set of standard case studies. The study considers the model checkers ETMCC, MRMC, PRISM (sparse and hybrid mode), YMER and VESTA, and focuses on fully probabilistic systems. Several of our experiments show significantly different run times and memory consumptions between the tools-up to various orders of magnitude--without, however, indicating a clearly dominating tool. For statistical model checking YMER clearly prevails whereas for the numerical tools MRMC and PRISM (sparse) are rather close.

Requirements-level semantics and model checking of object-oriented statecharts

In this paper we define a requirements-level execution semantics for object-oriented statecharts and show how properties of a system specified by these statecharts can be model checked using tool support for model checkers. Our execution semantics is requirements-level because it uses the perfect technology assumption, which abstracts from limitations imposed by an implementation. Statecharts describe object life cycles. Our semantics includes synchronous and asynchronous communication between objects and creation and deletion of objects. Our tool support presents a graphical front-end to model checkers, making these tools usable to people who are not specialists in model checking. The model-checking approach presented in this paper is embedded in an informal but precise method for software requirements and design. We discuss some of our experiences with model checking.

From StoCharts to MoDeST: a comparative reliability analysis of train radio communications

StoCharts have been proposed as a UML statechart extension for performance and dependability evaluation, and have been applied in the context of train radio reliability assessment to show the principal tractability of realistic cases with this approach. In this paper, we extend on this bare feasibility result in two important directions. First, we sketch the cornerstones of a mechanizable translation of StoCharts to MoDeST. The latter is a process algebra-based formalism supported by the MOTOR/MÖBIUS tool tandem. Second, we exploit this translation for a detailed analysis of the train radio case study.

Flow Faster: Efficient Decision Algorithms for Probabilistic Simulations

Strong and weak simulation relations have been proposed for Markov chains, while strong simulation and strong probabilistic simulation relations have been proposed for probabilistic automata. However, decision algorithms for strong and weak simulation over Markov chains, and for strong simulation over probabilistic automata are not efficient, which makes it as yet unclear whether they can be used as effectively as their non-probabilistic counterparts. This paper presents drastically improved algorithms to decide whether some (discrete- or continuous-time) Markov chain strongly or weakly simulates another, or whether a probabilistic automaton strongly simulates another. The key innovation is the use of parametric maximum flow techniques to amortize computations. We also present a novel algorithm for deciding strong probabilistic simulation preorders on probabilistic automata, which has polynomial complexity via a reduction to an LP problem. When extending the algorithms for probabilistic automata to their continuous-time counterpart, we retain the same complexity for both strong and strong probabilistic simulations.

A QoS-oriented extension of UML statecharts

Performance, dependability and quality of service (QoS) are prime aspects of the UML modeling domain. To capture these aspects effectively in a modeling language requires easy-to-use support for the specification and analysis of randomly varying behaviors. This paper introduces an extension of UML statecharts with randomly varying durations, by enriching a specific syntactic construct: The after operator is equipped with (discrete or continuous) probability distributions, determining the duration of the delay caused by this operator. The semantics of this extension is given in terms of a variant of stochastic automata. It is shown how existing model-checking tools can be used to calculate model-inherent QoS characteristics automatically. We study a UML model of an automatic teller machine scenario using this approach.

A Probabilistic Extension of UML Statecharts

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.