Abstract
In this paper, we present an approach to the generation of Petri nets exhibiting desired structural and behavioral properties. Given a reference Petri net, we apply a collection of local refinement transformations, which extends the internal structure of the reference model. The correctness of applying these transformations is justified via Petri net morphisms and by the fact that transformations do not add new deadlocks to Petri nets. We have designed two Petri net refinement algorithms supporting the randomized and fixed generation of models. These algorithms have been implemented and evaluated within the environment of the Carassius Petri net editor. The proposed approach can be applied to evaluate and conduct experiments for algorithms operating with Petri nets.
Highlights
Petri nets are widely used to formally represent the behavior of distributed systems for their precise semantics, which helps to prove many crucial behavioral properties, including boundedness, deadlock-freeness, covering by place invariants, and others [1]
Applying a collection of local transformations that extend the internal structure of a reference Petri net, we obtain a refinement exhibiting the same properties as an initial reference model
In this paper, we have presented an approach to the generation of Petri nets using structural propertypreserving transformations
Summary
Petri nets are widely used to formally represent the behavior of distributed systems for their precise semantics, which helps to prove many crucial behavioral properties, including boundedness, deadlock-freeness, covering by place invariants, and others [1]. The software implementation of algorithms operating with Petri nets naturally requires the preparation of model sets that exhibit the specific structural and behavioral properties. Such sets of models are used for the thorough evaluation of algorithms under development. This model has all the target structural and behavioral properties. Applying a collection of local transformations that extend the internal structure of a reference Petri net, we obtain a refinement exhibiting the same properties as an initial reference model. The mathematical framework of these transformations is responsible for preserving the structural and behavioral properties of a reference Petri net.
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