Document Open Access Logo

An Efficient and Uniform CSP Solution Generator Generator

Authors Ghiles Ziat , Martin Pépin

PDF HTML 5

Files

Thumbnail PDF
PDF
LIPIcs.CP.2025.40.pdf
  • Filesize: 1.03 MB
  • 18 pages
HTML5 Logo HTML (experimental)
LIPIcs.CP.2025.40.html

Document Identifiers

Subject Classification

ACM Subject Classification
  • Computing methodologies → Randomized search
Keywords
  • Constraint Programming
  • Property-based Testing

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

Constraint-based random testing is a powerful technique which aims at generating random test cases to verify functional properties of a program. Its objective is to determine whether a function satisfies a given property for every possible input. This approach requires firstly defining the property to satisfy, then secondly to provide a "generator of inputs" able to feed the program with the inputs generated. Besides, function inputs often need to satisfy certain constraints to ensure the function operates correctly, which makes the crafting of such a generator a hard task. In this paper, we are interested in the problem of manufacturing a uniform and efficient generator for the solutions of a CSP. In order to do that, we propose a specialized solving method that produces a well-suited representation for random sampling. Our solving method employs a dedicated propagation scheme based on the hypergraph representation of a CSP, and a custom split heuristic called birdge-first that emphasizes the interests of our propagation scheme. The generators we build are general enough to handle a wide range of use-cases. They are moreover uniform by construction, iterative and self-improving. We present a prototype built upon the AbSolute constraint solving library and demonstrate its performances on several realistic examples.

Cite As Get BibTex

Ghiles Ziat and Martin Pépin. An Efficient and Uniform CSP Solution Generator Generator. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 40:1-40:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.CP.2025.40

Author Details

Ghiles Ziat
  • EPITA Reasearch Lab (LRE), Le Kremlin-Bicêtre, France
Martin Pépin
  • Université Caen Normandie, ENSICAEN, CNRS, Normandie Univ, GREYC UMR 6072, F-14000 Caen, France

References

  1. Supratik Chakraborty, Kuldeep S. Meel, and Moshe Y. Vardi. A scalable and nearly uniform generator of SAT witnesses. CoRR, abs/1304.1584, 2013. URL: https://arxiv.org/abs/1304.1584.
  2. Koen Claessen, Jonas Duregård, and Michał H. Pałka. Generating constrained random data with uniform distribution. In Michael Codish and Eijiro Sumii, editors, Functional and Logic Programming, pages 18-34, Cham, 2014. Springer International Publishing. Google Scholar
  3. Koen Claessen and John Hughes. Quickcheck: a lightweight tool for random testing of haskell programs. SIGPLAN Not., 35(9):268-279, September 2000. URL: https://doi.org/10.1145/357766.351266.
  4. Simon Cruanes. Qcheck: Property-based testing for ocaml. https://github.com/c-cube/qcheck. Accessed: 2025-04-02.
  5. Emanuele De Angelis, Fabio Fioravanti, Adrián Palacios, Alberto Pettorossi, and Maurizio Proietti. Property-based test case generators for free. In Dirk Beyer and Chantal Keller, editors, Tests and Proofs, pages 186-206, Cham, 2019. Springer International Publishing. URL: https://doi.org/10.1007/978-3-030-31157-5_12.
  6. Leonardo de Moura and Nikolaj Bjørner. Z3: An efficient smt solver. In C. R. Ramakrishnan and Jakob Rehof, editors, Tools and Algorithms for the Construction and Analysis of Systems, pages 337-340, Berlin, Heidelberg, 2008. Springer Berlin Heidelberg. Google Scholar
  7. Rina Dechter, Kalev Kask, Eyal Bin, and Roy Emek. Generating random solutions for constraint satisfaction problems. In AAAI/IAAI, 2002. Google Scholar
  8. Philippe Duchon, Philippe Flajolet, Guy Louchard, and Gilles Schaeffer. Boltzmann samplers for the random generation of combinatorial structures. Combinatorics, Probability & Computing, 13(4-5):577-625, 2004. URL: https://doi.org/10.1017/S0963548304006315.
  9. Peter Dybjer, Qiao Haiyan, and Makoto Takeyama. Random generators for dependent types. In Zhiming Liu and Keijiro Araki, editors, Theoretical Aspects of Computing - ICTAC 2004, pages 341-355, Berlin, Heidelberg, 2005. Springer Berlin Heidelberg. Google Scholar
  10. Stefano Ermon, Carla P. Gomes, and Bart Selman. Uniform solution sampling using a constraint solver as an oracle. CoRR, abs/1210.4861, 2012. URL: https://arxiv.org/abs/1210.4861.
  11. Vibhav Gogate and Rina Dechter. A new algorithm for sampling csp solutions uniformly at random. In International Conference on Principles and Practice of Constraint Programming, pages 711-715, September 2006. URL: https://doi.org/10.1007/11889205_56.
  12. Carla Gomes and Meinolf Sellmann. Streamlined constraint reasoning. In Mark Wallace, editor, Principles and Practice of Constraint Programming - CP 2004, pages 274-289, Berlin, Heidelberg, 2004. Springer Berlin Heidelberg. URL: https://doi.org/10.1007/978-3-540-30201-8_22.
  13. Arnaud Gotlieb and Matthieu Petit. A uniform random test data generator for path testing. Journal of Systems and Software, 83(12):2618-2626, 2010. TAIC PART 2009 - Testing: Academic & Industrial Conference - Practice And Research Techniques. URL: https://doi.org/10.1016/j.jss.2010.08.021.
  14. W. K. Hastings. Monte carlo sampling methods using markov chains and their applications. Biometrika, 57(1):97-109, 1970. URL: http://www.jstor.org/stable/2334940.
  15. Kun He, Chunyang Wang, and Yitong Yin. Sampling lovász local lemma for general constraint satisfaction solutions in near-linear time. In 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), pages 147-158, 2022. URL: https://doi.org/10.1109/FOCS54457.2022.00021.
  16. David Albert Huffman. A method for the construction of minimum-redundancy codes. Proceedings of the IRE, 40(9):1098-1101, 1952. Google Scholar
  17. Leonidas Lampropoulos, Zoe Paraskevopoulou, and Benjamin C. Pierce. Generating good generators for inductive relations. Proc. ACM Program. Lang., 2(POPL), December 2017. URL: https://doi.org/10.1145/3158133.
  18. Luca Martino. Parsimonious adaptive rejection sampling. Electronics Letters, 53, June 2017. URL: https://doi.org/10.1049/el.2017.1711.
  19. Luca Martino and Joaquín Míguez. A generalization of the adaptive rejection sampling algorithm. Statistics and Computing, 21:633-647, 2010. URL: https://doi.org/10.1007/S11222-010-9197-9.
  20. Albert Nijenhuis and Herbert Wilf. Combinatorial Algorithms: For Computers and Hard Calculators. Academic Press, Inc., USA, 2nd edition, 1978. URL: https://doi.org/10.1016/C2013-0-11243-3.
  21. Marie Pelleau, Antoine Miné, Charlotte Truchet, and Frédéric Benhamou. A constraint solver based on abstract domains. In Roberto Giacobazzi, Josh Berdine, and Isabella Mastroeni, editors, Verification, Model Checking, and Abstract Interpretation, pages 434-454, Berlin, Heidelberg, 2013. Springer Berlin Heidelberg. URL: https://doi.org/10.1007/978-3-642-35873-9_26.
  22. Gilles Pesant, Claude-Guy Quimper, and Hélène Verhaeghe. Practically uniform solution sampling in constraint programming. In Pierre Schaus, editor, Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pages 335-344, Cham, 2022. Springer International Publishing. URL: https://doi.org/10.1007/978-3-031-08011-1_22.
  23. Quentin Plazar, Mathieu Acher, Gilles Perrouin, Xavier Devroey, and Maxime Cordy. Uniform sampling of sat solutions for configurable systems: Are we there yet? In 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST), pages 240-251, 2019. URL: https://doi.org/10.1109/ICST.2019.00032.
  24. Guy L. Steele Jr. and Sebastiano Vigna. Lxm: Better splittable pseudorandom number generators (and almost as fast). Proc. ACM Program. Lang., 5(OOPSLA), October 2021. URL: https://doi.org/10.1145/3485525.
  25. R.Endre Tarjan. A note on finding the bridges of a graph. Information Processing Letters, 2(6):160-161, 1974. URL: https://doi.org/10.1016/0020-0190(74)90003-9.
  26. Mathieu Vavrille, Charlotte Truchet, and Charles Prud'homme. Solution Sampling with Random Table Constraints. In Laurent D. Michel, editor, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021), volume 210 of Leibniz International Proceedings in Informatics (LIPIcs), pages 56:1-56:17, Dagstuhl, Germany, 2021. Schloss Dagstuhl - Leibniz-Zentrum für Informatik. URL: https://doi.org/10.4230/LIPIcs.CP.2021.56.
  27. Ke Xu, Frédéric Boussemart, Fred Hemery, and Christophe Lecoutre. Random constraint satisfaction: Easy generation of hard (satisfiable) instances. Artificial Intelligence, 171(8):514-534, 2007. URL: https://doi.org/10.1016/j.artint.2007.04.001.
  28. J. Yuan, K. Shultz, C. Pixley, H. Miller, and A. Aziz. Modeling design constraints and biasing in simulation using bdds. In 1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051), pages 584-589, 1999. URL: https://doi.org/10.1109/ICCAD.1999.810715.
  29. Ghiles Ziat, Matthieu Dien, and Vincent Botbol. Automated Random Testing of Numerical Constrained Types. In Laurent D. Michel, editor, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021), volume 210 of Leibniz International Proceedings in Informatics (LIPIcs), pages 59:1-59:19, Dagstuhl, Germany, 2021. Schloss Dagstuhl - Leibniz-Zentrum für Informatik. URL: https://doi.org/10.4230/LIPIcs.CP.2021.59.
  30. Ghiles Ziat, Marie Pelleau, Charlotte Truchet, and Antoine Miné. Finding solutions by finding inconsistencies. In CP 2018 - 24th International Conference on Principles and Practice of Constraint Programming, pages 1-16, Lille, France, August 2018. URL: https://hal.science/hal-01885769.
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact