State-space support for path-based reward variables
IPDS '98 Proceedings of the third IEEE international performance and dependability symposium on International performance and dependability symposium
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On the Logical Characterisation of Performability Properties
ICALP '00 Proceedings of the 27th International Colloquium on Automata, Languages and Programming
Towards Model Checking Stochastic Process Algebra
IFM '00 Proceedings of the Second International Conference on Integrated Formal Methods
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The Möbius Modeling Environment: Recent Developments
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
Mixing logics and rewards for the component-oriented specification of performance measures
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Theoretical Computer Science
Performance Trees: Expressiveness and Quantitative Semantics
QEST '07 Proceedings of the Fourth International Conference on Quantitative Evaluation of Systems
Specification patterns for probabilistic quality properties
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Principles of Model Checking (Representation and Mind Series)
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A Process Algebraic Approach to Software Architecture Design
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Proceedings of the 7th international conference on Formal methods for performance evaluation
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SFM'07 Proceedings of the 7th international conference on Formal methods for performance evaluation
Performability assessment by model checking of Markov reward models
Formal Methods in System Design
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An integral part of the performance modeling process is the specification of the performability measures of interest. The notations proposed for this purpose can be grouped into classes that differ from each other in their expressiveness and usability. Two representative notations are the continuous stochastic reward logic CSRL and the measure specification language MSL. The former is a stochastic temporal logic formulating quantitative properties about states and paths, while the latter is a component-oriented specification language relying on a first-order logic for defining reward-based measures. In this paper, we combine CSRL and MSL in order to take advantage of the expressiveness of the former and the usability of the latter. To this aim, we develop a unified notation in which the core logic of MSL is employed to set up the reward structures needed in CSRL, whereas themeasure definition mechanism of MSL is exploited to formalize measure and property specification patterns in a component-oriented fashion.