Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Abstract : Probabilistic verification techniques have been applied to the formal modelling and analysis of a wide range of systems, from commu- nication protocols such as Bluetooth, to nanoscale computing devices, to biological cellular processes. In order to tackle the inherent challenge of scalability, compositional approaches to verification are sorely needed. An example is assume-guarantee reasoning, where each component of a system is analysed independently, using assumptions about the other components that it interacts with. We discuss recent developments in the area of automated compositional verification techniques for probabilistic systems. In particular, we describe techniques to automatically generate probabilistic assumptions that can be used as the basis for compositional reasoning. We do so using algorithmic learning techniques, which have already proved to be successful for the generation of assumptions for compositional verification of non-probabilistic systems. We also present recent improvements and extensions to this work and survey some of the promising potential directions for further research in this area.
Type de document :
Communication dans un congrès
D. Giannakopoulou and F. Orejas. 14th International Conference on Fundamental Approaches to Software Engineering (FASE'11), 2011, Saarbrücken, Germany. Springer, 6603, pp.2--17, 2011, LNCS
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Contributeur : Hongyang Qu <>
Soumis le : jeudi 1 décembre 2011 - 14:15:06
Dernière modification le : mardi 6 décembre 2011 - 10:38:34
Document(s) archivé(s) le : dimanche 4 décembre 2016 - 16:07:20

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Lu Feng, Marta Kwiatkowska, David Parker. Automated Learning of Probabilistic Assumptions for Compositional Reasoning. D. Giannakopoulou and F. Orejas. 14th International Conference on Fundamental Approaches to Software Engineering (FASE'11), 2011, Saarbrücken, Germany. Springer, 6603, pp.2--17, 2011, LNCS. 〈hal-00647067〉

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