Synthetic portnet generation with controllable complexity for testing and benchmarking
conference paper
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.
Topics
TNO Identifier
968185
ISSN
16130073
Publisher
CEUR-WS
Source title
CEUR Workshop Proceedings, 2021 International Workshop on Petri Nets and Software Engineering, PNSE 2021, 25 June 2021
Editor(s)
Rolke, E.
Kohler-Bussmeier, H.
Kindler, M.
Kohler-Bussmeier, H.
Kindler, M.
Pages
195-212