MTGP: Combining Metamorphic Testing and Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Sobania:2023:EuroGP,
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author = "Dominik Sobania and Martin Briesch and
Philipp Roechner and Franz Rothlauf",
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title = "{MTGP}: Combining Metamorphic Testing and Genetic
Programming",
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booktitle = "EuroGP 2023: Proceedings of the 26th European
Conference on Genetic Programming",
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year = "2023",
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month = "12-14 " # apr,
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editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
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series = "LNCS",
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volume = "13986",
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publisher = "Springer Verlag",
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address = "Brno, Czech Republic",
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pages = "324--338",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Program
Synthesis, Metamorphic Testing: Poster",
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isbn13 = "978-3-031-29572-0",
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URL = "https://rdcu.be/c8U3M",
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DOI = "doi:10.1007/978-3-031-29573-7_21",
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size = "15 pages",
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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.",
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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