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Finding Minimum and Maximum Termination Time of Timed Automata Models with Cyclic Behaviour

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 Added by Omar Al-Bataineh
 Publication date 2016
and research's language is English




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The paper presents a novel algorithm for computing best and worst case execution times (BCET/WCET) of timed automata models with cyclic behaviour. The algorithms can work on any arbitrary diagonal-free TA and can handle more cases than previously existing algorithms for BCET/WCET computations, as it can handle cycles in TA and decide whether they lead to an infinite WCET. We show soundness of the proposed algorithm and study its complexity. To our knowledge, this is the first model checking algorithm that addresses comprehensively the BCET/WCET problem of systems with cyclic behaviour. Behrmann et al. provide an algorithm for computing the minimum cost/time of reaching a goal state in priced timed automata (PTA). The algorithm has been implemented in the well-known model checking tool UPPAAL to compute the minimum time for termination of an automaton. However, we show that in certain circumstances, when infinite cycles exist, the algorithm implemented in UPPAAL may not terminate, and we provide examples which UPPAAL fails to verify.



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The paper addresses the problem of computing maximal expected time to termination of probabilistic timed automata (PTA) models, under the condition that the system will, eventually, terminate. This problem can exhibit high computational complexity, in particular when the automaton under analysis contains cycles that may be repeated very often (due to very high probabilities, e.g. p =0.999). Such cycles can degrade the performance of typical model checking algorithms, as the likelihood of repeating the cycle converges to zero arbitrarily slowly. We introduce an acceleration technique that can be applied to improve the execution of such cycles by collapsing their iterations. The acceleration process of a cyclic PTA consists of several formal steps necessary to handle the cumulative timing and probability information that result from successive executions of a cycle. The advantages of acceleration are twofold. First, it helps to reduce the computational complexity of the problem without adversely affecting the outcome of the analysis. Second, it can bring the worst case execution time problem of PTAs within the bounds of feasibility for model checking techniques. To our knowledge, this is the first work that addresses the problem of accelerating execution of cycles that exhibit both timing and probabilistic behavior.
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