Title
Synthetic portnet generation with controllable complexity for testing and benchmarking
Author
Diallo, M.
Akesson, B.
Bera, D.
Begeer, R.
Contributor
Rolke, E. (editor)
Kohler-Bussmeier, H. (editor)
Kindler, M. (editor)
Publication year
2021
Abstract
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.
Subject
Application programs
Benchmarking
Open source software
Petri nets
Analysis and synthesis
Input and outputs
Non Determinism
Open sources
Random Model
Synthetic generation
Heuristic methods
To reference this document use:
http://resolver.tudelft.nl/uuid:51ff625e-a114-43aa-a8fc-b75d79640c24
TNO identifier
968185
Publisher
CEUR-WS
ISSN
1613-0073
Source
CEUR Workshop Proceedings, 2021 International Workshop on Petri Nets and Software Engineering, PNSE 2021, 25 June 2021, 195-212
Document type
conference paper