2026-07-26T04:38:47Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:20714 2024-08-29T08:52:52Z ddc:004 open_access

Mutational Fuzz Testing for Constraint Modeling Systems Vanroose, Wout Bleukx, Ignace Devriendt, Jo Tsouros, Dimos Verhaeghe, Hélène Guns, Tias fuzz testing Constraint modeling language bugs mutational testing modeling constraint reformulation Constraint programming (CP) modeling languages, like MiniZinc, Essence and CPMpy, play a crucial role in making CP technology accessible to non-experts. Both solver-independent modeling frameworks and solvers themselves are complex pieces of software that can contain bugs, which undermines their usefulness. Mutational fuzz testing is a way to test complex systems by stochastically mutating input and verifying preserved properties of the mutated output. We investigate different mutations and verification methods that can be used on the constraint specifications directly. This includes methods proposed in the context of SMT problem specifications, as well as new methods related to global constraints, optimization, and solution counting/preservation. Our results show that such a fuzz testing approach improves the overall code coverage of a modeling system compared to only unit testing, and is able to find bugs in the whole toolchain, from the modeling language transformations themselves to the underlying solvers. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Wout Vanroose and Ignace Bleukx and Jo Devriendt and Dimos Tsouros and Hélène Verhaeghe and Tias Guns 2024 Is Part Of LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.CP.2024.29 urn:nbn:de:0030-drops-207149 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.29 eng https://creativecommons.org/licenses/by/4.0/legalcode