Abstract
The efficacy of decision diagram techniques for state space generation is known to be heavily dependent on the variable order. Ordering can be done a-priori (static) or during the state space generation (dynamic). We focus our attention on static ordering techniques. Many static decision diagram variable ordering techniques exist, but it is hard to choose which method to use, since only fragmented performance information is available. In the work reported in this paper we used the models of the Model Checking Contest 2017 edition to conduct an extensive comparison of 18 different algorithms, in order to better understand their efficacy. Comparison is based on the size of the decision diagram of the reachable state space, which is generated using the Saturation method provided by the Meddly library.
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Notes
- 1.
We actually use a simplified version where weights, model categories and answer correctness are not considered.
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We would like to thank the MCC team and all colleagues that collaborated with them for the construction of the MCC database of models, and the Meddly library developers.
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Amparore, E.G., Donatelli, S., Beccuti, M., Garbi, G., Miner, A. (2018). Decision Diagrams for Petri Nets: A Comparison of Variable Ordering Algorithms. In: Koutny, M., Kristensen, L., Penczek, W. (eds) Transactions on Petri Nets and Other Models of Concurrency XIII. Lecture Notes in Computer Science(), vol 11090. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58381-4_4
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