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The Markov Reward Model Checker (MRMC) is a software tool for verifying properties over probabilistic models. It supports PCTL and CSL model checking, and their reward extensions. Distinguishing features of MRMC are its support for computing time-and reward-bounded reachability probabilities, (property-driven) bisimulation minimization, and precise(More)
ion techniques based on simulation relations have become an important and effective proof technique to avoid the infamous state space explosion problem. In the context of Markov chains, strong and weak simulation relations have been proposed [17, 6], together with corresponding decision algorithms [3, 5], but it is as yet unclear whether they can be used as(More)
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(More)
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(More)
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(More)
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(More)
This paper presents an algorithm for cost-bounded probabilistic reachability in timed automata extended with prices (on edges and locations) and discrete probabilistic branching. The algorithm determines whether the probability to reach a (set of) goal location(s) within a given price bound (and time bound) can exceed a threshold p ∈ [0, 1]. We prove that(More)
The branching-time temporal logic PCTL * has been introduced to specify quantitative properties over probability systems, such as discrete-time Markov chains. Until now, however, no logics have been defined to specify properties over hidden Markov models (HMMs). In HMMs the states are hidden, and the hidden processes produce a sequence of observations. In(More)