Download Assurances for Self-Adaptive Systems: Principles, Models, by Maxime Cordy, Andreas Classen, Patrick Heymans, Axel Legay, PDF

By Maxime Cordy, Andreas Classen, Patrick Heymans, Axel Legay, Pierre-Yves Schobbens (auth.), Javier Cámara, Rogério de Lemos, Carlo Ghezzi, Antónia Lopes (eds.)

The expanding complexity of platforms and the starting to be uncertainty of their operational environments have created a serious have to increase structures capable of increase their operation, adapt to alter, and get over mess ups autonomously. this case has ended in contemporary advances in self-adaptive structures capable of reconfigure their constitution and alter their habit at run-time to evolve to environmental adjustments. regardless of those advances, one key element of self-adaptive structures that is still to be tackled intensive is "assurances": the availability of facts that the procedure satisfies its said useful and non-functional standards in the course of its operation within the presence of self-adaptation. This ebook is without doubt one of the results of the ESEC/FSE 2011 Workshop on Assurances for Self-Adaptive platforms (ASAS), held in Szeged, Hungary, in September 2011. It comprises prolonged models of a few of the papers provided in the course of the workshop, in addition to invited papers from famous specialists. The 12 refereed papers have been completely reviewed and chosen. The booklet contains 4 components: formal verification, versions and middleware, failure prediction, and coverage techniques.

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Extra resources for Assurances for Self-Adaptive Systems: Principles, Models, and Techniques

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An effective design of effective and efficient self-adaptive systems may rely on several existing approaches. Software models and model checking techniques at run time represent one of them since they support automatic reasoning about such changes, detect harmful configurations, and potentially enable appropriate (self-)reactions. However, traditional model checking techniques and tools may not be applied as they are at run time, since they hardly meet the constraints imposed by on-the-fly analysis, in terms of execution time and memory occupation.

The first consists in the parametric analysis of the model with respect to the desired property, whose result is a closed mathematical expression depending on the symbolic variables appearing in the model. This step is quite complex in terms of computational time, but it can be accomplished once for all at design-time, when time is usually not a strong constraint. At runtime all that is needed to obtain the actual analysis response is to replace the symbolic variables with the actual values provided from modeling, as soon as they are discovered.

J. C´ amara et al. ): Assurances for Self-Adaptive Systems, LNCS 7740, pp. 30–59, 2013. c Springer-Verlag Berlin Heidelberg 2013 Probabilistic Verification at Runtime for Self-Adaptive Systems 31 Designers must ensure that any critical requirement of the system continues to be satisfied before, during and after unforeseen scenarios. By this we mean that software systems are required to be dependable, to avoid damaging effects due to violated requirements that can range from loss of business to loss of human lives.

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