Generating benchmarks by random stepwise refinement of Petri nets
Kees M. van Hee, Zheng Liu · TU/e Research Portal · 2012
The quality of algorithms is often determined by benchmarking, i.e., testing the algorithm on a predetermined data set. In contrast to traditional benchmarking, with fixed data set, we present a way to generate random sets of test data. In this paper we present random classes of Petri nets and a method to generate finite samples from such a class. The classes may contain infinitely many Petri nets, each net with its own probability to be generated. This generation method is based on stepwise application of construction rules such as refinement rules. Each random class of Petri nets has a probability distribution for each of its characteristics. We illustrate the approach by estimating this distribution for some simple characteristics.