The how and why of interactive Markov chains
FMCO'09 Proceedings of the 8th international conference on Formal methods for components and objects
Bisimulations meet PCTL equivalences for probabilistic automata
CONCUR'11 Proceedings of the 22nd international conference on Concurrency theory
A general framework for probabilistic characterizing formulae
VMCAI'12 Proceedings of the 13th international conference on Verification, Model Checking, and Abstract Interpretation
Quantitative timed analysis of interactive markov chains
NFM'12 Proceedings of the 4th international conference on NASA Formal Methods
Model checking of scenario-aware dataflow with CADP
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
Concurrency meets probability: theory and practice
CONCUR'13 Proceedings of the 24th international conference on Concurrency Theory
Confluence reduction for markov automata
FORMATS'13 Proceedings of the 11th international conference on Formal Modeling and Analysis of Timed Systems
Modelling, reduction and analysis of markov automata
QEST'13 Proceedings of the 10th international conference on Quantitative Evaluation of Systems
A compositional model to reason about end-to-end QoS in Stochastic Reo connectors
Science of Computer Programming
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Fault trees (FTs) are among the most prominent formalisms for reliability analysis of technical systems. Dynamic FTs extend FTs with support for expressing dynamic dependencies among components. The standard analysis vehicle for DFTs is state-based, and treats the model as a continuous-time Markov chain (CTMC). This is not always possible, as we will explain, since some DFTs allow multiple interpretations. This paper introduces a rigorous semantic interpretation of DFTs. The semantics is defined in such a way that the semantics of a composite DFT arises in a transparent manner from the semantics of its components. This not only eases the understanding of how the FT building blocks interact. It is also a key to alleviate the state explosion problem. By lifting a classical aggregation strategy to our setting, we can exploit the DFT structure to build the smallest possible Markov chain representation of the system. The semantics—as well as the aggregation and analysis engine is implemented in a tool, called CORAL. We show by a number of realistic and complex systems that this methodology achieves drastic reductions in the state space.