Johann Schuster

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CASPA is a stochastic process algebra tool for performance and dependability modelling, analysis and verification. It is based entirely on the symbolic data structure MTBDD (multi-terminal binary decision diagram) which enables the tool to handle models with very large state space. This paper describes an extension of CASPA's solving engine for path-based(More)
This paper presents several symbolic counterexample generation algorithms for discrete-time Markov chains (DTMCs) violating a PCTL formula. A counterexample is (a symbolic representation of) a sub-DTMC that is incrementally generated. The crux to this incremental approach is the symbolic generation of paths that belong to the counterexample. We consider two(More)
Peripheral nerve stimulation limits the use of whole-body gradient systems capable of slew rates > 80 T/m/s and gradient strengths > 25 mT/m. The stimulation threshold depends mainly on the amplitude of the induced electric field in the patient's body, and thus can be influenced by changing the total magnetic flux of the gradient coil. A gradient system was(More)
One of the prevailing ideas in applied concurrency theory and verification is the concept of automata minimization with respect to strong or weak bisimilarity. The minimal automata can be seen as canonical representations of the behaviour modulo the bisimilarity considered. Together with congruence results wrt. process algebraic operators, this can be(More)
CASPA Composition and Analysis of Stochastic Process Algebra stochastic process algebra tool performance and dependability modelling, analysis and verification. Markovian transitions negative exponentially distributed transitions used for modelling delays specification by rates Immediate transitions used for modelling timeless actions (e.g.(More)
This paper presents LARES, a novel approach to the modeling of fault-tolerant systems. We introduce a formalism for describing the structure of a system which is able to express dynamic behavior such as imperfect coverage, common cause errors, failure propagation, increase of failure rates after partial system failure, and phased missions. It is designed(More)
This paper develops a decision algorithm for weak bisimulation on Markov Automata (MA). For this purpose, different notions of vanishing states (a concept originating from the area of Generalised Stochastic Petri Nets) are defined. In particular, non-näıvely vanishing states are shown to be essential for relating the concepts of (state-based) näıve weak(More)
In this paper we investigate the generation of counterexamples for discrete-time Markov chains (DTMCs) and PCTL properties. Whereas most available methods use explicit representations for at least some intermediate results, our aim is to develop fully symbolic algorithms. As in most related work, our counterexample computations are based on path search. We(More)