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In this paper, we present a combination of symbolic execution [26, 27] and real-time model checking that generates a precise control-flow model from the symbolic execution trees obtained with a symbolic execution of the program.
We propose solutions for these problems and show by means of model checking that the modified system meets the requirements.
It is also well known in model checking that bisimulation is equivalent to strong preservation of CTL or, equivalently, of Hennessy Milner logic.
Given the formal model of the system in terms of Kripke structures, it is possible to verify with model checking that even in case of faults the system do not violate given safety and liveness properties.
The use of agency logics may be able to provide proof by axioms or by model checking that the protocols give the correct results and are achievable.
We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark datasets.
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These analyses may then be run against software specifications, encapsulated as models, checking that they are sound with respect to the given ontology.
This confirms the observing response task as a potential model of checking that has relevance to OCD studies.
The final outcome is a rich formalism to represent and reason about relevant concepts in artificial intelligence, while still having a model checking problem that is no more computationally expensive than that of the less expressive quantified boolean logic.
In this work, we make use of a model checking approach that is based on a model transformation to validate the automatically derived behavior of a WBSN for health monitoring.
Symbolic algorithms are successfully used for model checking systems that have very large state spaces [ 48].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com