MTGP: Combining Metamorphic Testing and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Sobania:2023:EuroGP,

• author = "Dominik Sobania and Martin Briesch and Philipp Roechner and Franz Rothlauf",
• title = "{MTGP}: Combining Metamorphic Testing and Genetic Programming",
• booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming",
• year = "2023",
• month = "12-14 " # apr,
• editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
• series = "LNCS",
• volume = "13986",
• publisher = "Springer Verlag",
• address = "Brno, Czech Republic",
• pages = "324--338",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Program Synthesis, Metamorphic Testing: Poster",
• isbn13 = "978-3-031-29572-0",
• URL = "https://rdcu.be/c8U3M",
• DOI = "doi:10.1007/978-3-031-29573-7_21",
• size = "15 pages",
• abstract = "Genetic programming is an evolutionary approach known for its performance in program synthesis. However, it is not yet mature enough for a practical use in real-world software development, since usually many training cases are required to generate programs that generalize to unseen test cases. As in practice, the training cases have to be expensively hand-labeled by the user, we need an approach to check the program behavior with a lower number of training cases. Metamorphic testing needs no labeled input/output examples. Instead, the program is executed multiple times, first on a given (randomly generated) input, followed by related inputs to check whether certain user-defined relations between the observed outputs hold. In this work, we suggest MTGP, which combines metamorphic testing and genetic programming and study its performance and the generalizability of the generated programs. Further, we analyze how the generalizability depends on the number of given labeled training cases. We find that using metamorphic testing combined with labeled training cases leads to a higher generalization rate than the use of labeled training cases alone in almost all studied configurations. Consequently, we recommend researchers to use metamorphic testing in their systems if the labeling of the training data is expensive.",
• notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023",

}

Genetic Programming entries for Dominik Sobania Martin Briesch Philipp Roechner Franz Rothlauf

Citations