Compositional Dependability Evaluation for STATEMATE

Böde, Eckard; Herbstritt, Marc; Hermanns, Holger; Johr, Sven; Peikenkamp, Thomas; Pulungan, Reza; Rakow, Jan; Wimmer, Ralf; Becker, Bernd

doi:10.1109/tse.2008.102

Cited by 47 publications

(52 citation statements)

References 26 publications

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“…IMCs turn out to be a natural semantic model for AADL [5]; the use of this connection in the aerospace domain has recently been shown in [29]. In addition, IMCs are used for stochastic extensions of Statemate [3], and for modeling and analysing industrial GALS hardware designs [12].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Timed Analysis of Interactive Markov Chains

Guck

Han

Katoen

et al. 2012

Lecture Notes in Computer Science

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Abstract. This paper presents new algorithms and accompanying tool support for analyzing interactive Markov chains (IMCs), a stochastic timed 1 1 2 -player game in which delays are exponentially distributed. IMCs are compositional and act as semantic model for engineering formalisms such as AADL and dynamic fault trees. We provide algorithms for determining the extremal expected time of reaching a set of states, and the long-run average of time spent in a set of states. The prototypical tool Imca supports these algorithms as well as the synthesis of ε-optimal piecewise constant timed policies for timed reachability objectives. Two case studies show the feasibility and scalability of the algorithms.

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Section: Introductionmentioning

confidence: 99%

Quantitative Timed Analysis of Interactive Markov Chains

Guck

Han

Katoen

et al. 2012

Lecture Notes in Computer Science

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“…The developed technology was applied to a non-trivial case study from the train control domain with an explication of the improvements contributed by each of the relevant translation steps. In Table 5 we quote a fraction of the relevant information from [8], illustrating the effects and costs of compositional minimisation. such as PEPA [43], EMPA [6] or MTIPP [41], the latter being the semantic basis of the PRISM toolkit [44] in 'ctmc' mode.…”

Section: Examplementioning

confidence: 99%

“…First and foremost, IMCs have shown their practical relevance in applica-tions of various domains, ranging from dynamic fault trees [11,10,12], architectural description languages such as AADL (Architectural Analysis and Design Language) [9,15,13,14], generalised stochastic Petri nets [40] and Statemate [8] to GALS (Globally Asynchronous Locally Synchronous) hardware design [22,19,23]. The availability of CTMC-based tool support [31] for IMCs has led to several of these applications.…”

Section: Introductionmentioning

confidence: 99%

The How and Why of Interactive Markov Chains

Hermanns

Katoen

2010

Formal Methods for Components and Objects

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Abstract. This paper reviews the model of interactive Markov chains (IMCs, for short), an extension of labelled transition systems with exponentially delayed transitions. We show that IMCs are closed under parallel composition and hiding, and show how IMCs can be compositionally aggregated prior to analysis by e.g., bisimulation minimisation or aggressive abstraction based on simulation pre-congruences. We survey some recent analysis techniques for IMCs, i.e., explaining how measures such as reachability probabilities can be obtained. Finally, we demonstrate that IMCs are a natural (and simple) semantic model for stochastic process algebras and generalised stochastic Petri nets and can be used for engineering formalisms such as AADL and dynamic fault trees.

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“…In an MTBDD-based implementation, such as CUDD, the same set of MTBDD variables are used to represent all entities, that is, matrices and sets of states. In our symbolic algorithm, we will need to represent states using either the variable set vr 1 B(t, s)) does not depend on t in row (resp. column) encoding we use B(s) as a shorthand.…”

Section: Multi-terminal Binary Decision Diagrammentioning

confidence: 99%

“…The first obvious method is to store each class of a partition as a BDD. Another technique, given in [1], is to assign an extra set of BDD variables to denote class indices. In particular, s ∈ C i iff P(s, i) = 1 where P is the BDD representation of Π.…”

Section: Symbolic Representation Of Partitionsmentioning

confidence: 99%

A Symbolic Algorithm for Optimal Markov Chain Lumping

Derisavi

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. Many approaches to tackle the state explosion problem of Markov chains are based on the notion of lumpability, which allows computation of measures using the quotient Markov chain, which, in some cases, has much smaller state space than the original one. We present, for the first time, a symbolic algorithm and its implementation for the lumping of Markov chains that are represented using Multi-Terminal Binary Decision Diagrams. The algorithm is optimal, i.e., generates the smallest possible quotient Markov chain. Our experiments on various configurations of two example models show that the algorithm (1) handles significantly larger state spaces than an explicit algorithm, (2) is in the best case, faster than an efficient explicit algorithm while not prohibitively slower in the worst case, and (3) generates quotient Markov chains that are several orders of magnitude smaller than ones generated by a model-dependent symbolic lumping algorithm.

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Compositional Dependability Evaluation for STATEMATE

Cited by 47 publications

References 26 publications

Quantitative Timed Analysis of Interactive Markov Chains

Quantitative Timed Analysis of Interactive Markov Chains

The How and Why of Interactive Markov Chains

A Symbolic Algorithm for Optimal Markov Chain Lumping

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