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

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 structures and the transforming into uncertainty of their operational environments have created a severe have to strengthen structures capable of enhance their operation, adapt to alter, and get over disasters autonomously. this example has ended in contemporary advances in self-adaptive structures capable of reconfigure their constitution and adjust their habit at run-time to conform to environmental alterations. regardless of those advances, one key point of self-adaptive platforms that is still to be tackled intensive is "assurances": the supply of facts that the approach satisfies its said sensible and non-functional specifications in the course of its operation within the presence of self-adaptation. This booklet is without doubt one of the results of the ESEC/FSE 2011 Workshop on Assurances for Self-Adaptive structures (ASAS), held in Szeged, Hungary, in September 2011. It comprises prolonged models of a few of the papers offered 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 publication contains 4 elements: formal verification, versions and middleware, failure prediction, and coverage techniques.

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Tamburrelli XI =k (ω) = ρ(sk ) 0 XC ≤k (ω) = XF Φ (ω) = ⎧ 0 ⎪ ⎪ ⎨∞ ⎪ ⎪ ⎩ k−1 i=0 (2) if k = 0 ρ(si ) + ι(si , si+1 ) otherwise min{j|sj |=Φ}−1 i=0 if s0 |= Φ if ∀i si Φ (3) (4) ρ(si ) + ι(si , si+1 ) otherwise In Section 3 we will show how the value of XΘ can be computed with algebraic techniques taking into account the presence of both numeric values and variable parameters in the D-MRM model. Exploiting rewards we are able to express more complex requirements which may consider for example costs or latencies.

That is why we designed A-FTS with a low grain, where individual transitions can refer to features. As a consequence, first, features are spread over the whole A-FTS, and may be difficult to grasp; second, the addition of a supplementary feature is difficult, since each transition might need a revision. We plan thus to use (extensions of) the aspect-oriented approach to maintain a more localized and independent description of each feature (previous work on this topic includes [16,31,41]). The addition of a new feature will then hopefully be more understandable.

The intuition is that its value corresponds to the probability of taking a path that satisfies Ψ , among all the, possibly infinite, paths originating in s. π[k] |= Φ)) Let us now focus on the semantics of the rewards fragment of R-PCTL. We intuitively define how a state s can satisfy a formula R r (Θ) depending on the way the reward expression Θ is formulated. – R r (I =k ) is true in state s if the expected state reward to be gained in the state entered at step k along the paths originating in s meets the bound r.

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