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Improving swarming using genetic algorithms

  • S.I. : VeCoS 2018
  • Published:
Innovations in Systems and Software Engineering Aims and scope Submit manuscript

Abstract

The verification of temporal properties against a given system may require the exploration of its full state space. In explicit model checking, this exploration uses a depth-first search and can be achieved with multiple randomized threads to increase performance. Nonetheless, the topology of the state space and the exploration order can cap the speedup up to a certain number of threads. This paper proposes a new technique that aims to tackle this limitation by generating artificial initial states, using genetic algorithms. Threads are then launched from these states and thus explore different parts of the state space. Our prototype implementation is 10% faster than state-of-the-art algorithms on a general benchmark and 40% on a specialized benchmark. Even if we expected a decrease in an order of magnitude, these results are still encouraging since they suggest a new way to handle existing limitations. Empirically, our technique seems well suited for “linear” topology, i.e., the one we can obtain when combining model checking algorithms with partial-order reduction techniques.

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Notes

  1. It should be noted that even if DFS-based algorithms are hard to parallelize [31], they scale better in practice than parallelized breadth-first search (BFS) algorithms.

  2. Sect. 6 for more details about the benchmark.

  3. Notice that we use the C++ convention where visited.get(s) is written visited[s].

  4. This particular case will certainly degrade performance due to contention over the shared hashmap.

  5. Godefroid and Khurshid [15] do not generate states, but finite paths and their fitness functions analyze the whole paths to keep only those with few enabled transitions.

  6. Main differences have been highlighted to help the reader.

  7. Notice that mutation can be done ensuring that this variable will not be less than 1 and not be greater than 3.

  8. Here, we only describe our approach on Büchi automata, but the adaptation for generalized Büchi automata is straightforward.

  9. For a description of our setup, including selected models, detailed results and code, see http://www.lrde.epita.fr/~renault/benchs/ISSE-2020/results.html. All experiments can be replayed using either the Dockerfile available at https://github.com/etienne-renault/swarmedgp-docker or directly the Docker available at https://hub.docker.com/r/akaesus/swarmedgp. Also note that the archive of all our experiments has been published on Zenodo with (DOI): https://doi.org/10.5281/zenodo.3707234.

  10. See http://fmt.cs.utwente.nl/tools/ltsmin/#divine for more details. Also note that we added some patches (available in the web page) to manage out-of-bound detection.

  11. We evaluate other thresholds like 0.9999 or 0.99999, but it appears that augmenting the threshold does not increase performance, see the web page for more details.

  12. dve2lts-mc –strategy=cndfs –threads=... -s 29 –perm=shift.

  13. dve2lts-mc –strategy=ufscc –threads=... -s 29 –perm=shift.

  14. dve2lts–sym –saturation=sat –order=bfs-prev -rf –lace-workers=... .

  15. https://ltsmin.utwente.nl.

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  1. LRDE, EPITA, Le Kremlin-Bicêtre, France

    Etienne Renault

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Renault, E. Improving swarming using genetic algorithms. Innovations Syst Softw Eng 16, 143–159 (2020). https://doi.org/10.1007/s11334-020-00362-7

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