Download Automated Technology for Verification and Analysis: 14th by Cyrille Artho, Axel Legay, Doron Peled PDF

By Cyrille Artho, Axel Legay, Doron Peled

This e-book constitutes the lawsuits of the 14th foreign Symposium on computerized know-how for Verification and research, ATVA 2016, held in Chiba, Japan, in October 2016.

The 31 papers awarded during this quantity have been rigorously reviewed and chosen from eighty two submissions. They have been geared up in topical sections named: keynote; Markov types, chains, and determination methods; counter platforms, automata; parallelism, concurrency; complexity, decidability; synthesis, refinement; optimization, heuristics, partial-order mark downs; fixing methods, version checking; and software research.

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Extra info for Automated Technology for Verification and Analysis: 14th International Symposium, ATVA 2016, Chiba, Japan, October 17-20, 2016, Proceedings

Sample text

1 except for its objective function which is set to maximize t∈T ft · E(t). In other words, TE tries to maximize the long-run average change of the energy level under the constraints given in LE . Let g = (ge )e∈T be an optimal solution of TE , and let g ∗ be the corresponding optimal value of the objective function. Now we distinguish two cases, which require completely different proof techniques. Case A. g ∗ > 0. Case B. g ∗ = 0. We start with Case A. Note that if g ∗ > 0, then there exists a component D of g such that trend D ≥ g ∗ > 0.

There is only one strategy (the empty function), and it is easy to verify that assigning 1/4 to each variable in LE solves the linear program with e∈T fe · E(e) = 14 . However, for every m there is a positive probability that the decrementing loop on t is taken at least m times, and thus the strategy is not safe. Fig. 1. A linear program LE with non-negative variables fe , e ∈ T (left), and an EMDP where the strategy corresponding to the solution of LE is not safe (right). Each transition is labelled by the associated counter update (in boldface), reward, and probability Although the program LE does not directly yield a safe strategy optimizing the mean payoff, it is still useful for obtaining certain “building blocks” of such a strategy.

To provide a fair comparison we have run all experiments with the 30 A. Abate et al. same setting (except for the thresholds on the maximal error bounds). However, our observations show that a fine tuning of the parameters for a certain problem can lead to additional performance improvements. The overhead related to updating the aggregated model (by means of its transition matrix), proved to have a significant impact on the overall performance. Therefore, a dynamic data structure implementing the model can improve the performance.

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