A Markov Chain Model for Statistical Software Testing
IEEE Transactions on Software Engineering
PRISM 2.0: A Tool for Probabilistic Model Checking
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Early prediction of software component reliability
Proceedings of the 30th international conference on Software engineering
Model evolution by run-time parameter adaptation
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Using scenarios to predict the reliability of concurrent component-based software systems
FASE'05 Proceedings of the 8th international conference, held as part of the joint European Conference on Theory and Practice of Software conference on Fundamental Approaches to Software Engineering
Leveraging state-based user preferences in context-aware reconfigurations for self-adaptive systems
SEFM'11 Proceedings of the 9th international conference on Software engineering and formal methods
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Probabilistic models are useful in the analysis of system behaviour and non-functional properties. Reliable estimates and measurements of probabilities are needed to annotate behaviour models in order to generate accurate predictions. However, this may not be sufficient, and may still lead to inaccurate results when the system model does not properly reflect the probabilistic choices made by the environment. Thus, not only should the probabilities be accurate in properly reflecting reality, but also the model that is being used. In this paper we identify and illustrate this problem showing that it can lead to inaccuracies and both false positive and false negative property checks. We propose state refinement as a technique to mitigate this problem, and present a framework for iteratively improving the accuracy of a probabilistically annotated behaviour model.