Synthetic Portnet Generation with Controllable Complexity for Testing and Benchmarking

There are many classes of Petri nets for describing communicating systems. Some of these guarantee important properties, such as
termination in the case of portnets. There are also many methods and
tools available for their analysis and synthesis. However, when developing new methods, or benchmarking against existing ones, it is often
helpful to quickly generate large sets of random models satisfying certain
properties and user-defined rules.

This paper presents a heuristic-driven method for synthetic generation of
random portnets based on refinement rules. The method considers three
user-specified complexity parameters: the expected number input and
output places, and the prevalence of non-determinism in the skeleton
of the generated net. An implementation of this method is available
as an open-source Python tool. Experiments demonstrate the relations
between the three complexity parameters and investigate the boundaries
of the proposed method.