Evolutionary Generation of Metamorphic Relations for Cyber-Physical Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{ayerdi:2022:GECCOhop,

• author = "Jon Ayerdi and Valerio Terragni and Aitor Arrieta and Paolo Tonella and Goiuria Sagardui and Maite Arratibel",
• title = "Evolutionary Generation of Metamorphic Relations for {Cyber-Physical} Systems",
• booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion",
• year = "2022",
• editor = "Marcus Gallagher",
• pages = "15--16",
• address = "Boston, USA",
• series = "GECCO '22",
• month = "9-13 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, quality of service, cyber physical systems, metamorphic testing, oracle improvement, oracle generation, genetic programming, evolutionary algorithm, mutation testing, metamorphic testing",
• isbn13 = "978-1-4503-9268-6/22/07",
• URL = "https://valerio-terragni.github.io/assets/pdf/ayerdi-gecco-2022.pdf",
• DOI = "doi:10.1145/3520304.3534077",
• size = "2 pages",
• abstract = "A problem when testing Cyber-Physical Systems (CPS) is the difficulty of determining whether a particular system output or behaviour is correct or not. Metamorphic testing alleviates such a problem by reasoning on the relations expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. This paper summarizes our recent publication: {"}Generating Metamorphic Relations for Cyber-Physical Systems with Genetic Programming: An Industrial Case Study{"}, presented at ESEC/FSE 2021. In that publication we presented GAssertMRs, the first technique to automatically generate MRs for CPS, leveraging GP to explore the space of candidate solutions. We evaluated GAssertMRs in an industrial case study, outperforming other baselines.",
• notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Jon Ayerdi Valerio Terragni Aitor Arrieta Paolo Tonella Goiuria Sagardui Mendieta Maite Arratibel

Citations