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- W1792138840 abstract "A heuristic-based symbolic model checking algorithm, BDD-IDA*. that efficiently falsifies invariant properties of a system is presented. As in bounded model checking, the algorithm uses an iterative deepening search strategy. However, in our case, the search strategy is guided by a heuristic that is computed from an abstract model, which is derived from the concrete model by a synthesis technique. Synthesis involves eliminating so-called weak variables from the concrete specification, where the weak variables are identified by a data-dependency analysis. Unique to this work is the use of the depth-first IDA* search algorithm in a BDD setting, and the automatic synthesis of the heuristic. The performance of the approach on a large number of small examples is compared with standard BDD-based approaches. Experiments on a variety of real-world models from different domains are also conducted. The approach reveals a consistent improvement on all models, and in some cases a speed-up of 2 orders of magnitude is obtained." @default.
- W1792138840 created "2016-06-24" @default.
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- W1792138840 date "2005-01-01" @default.
- W1792138840 modified "2023-10-10" @default.
- W1792138840 title "Abstraction-Guided Model Checking Using Symbolic IDA* and Heuristic Synthesis" @default.
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- W1792138840 doi "https://doi.org/10.1007/11562436_21" @default.
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