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Title
Evolutionary generation of metamorphic relations for cyber-physical systemsOther institutions
University of AucklandUniversità della Svizzera italiana (USI) (Suiza)
Orona S.Coop.
Version
Postprint
Rights
© 2022 The AuthorsAccess
Open accessPublisher’s version
https://doi.org/10.1145/3520304.3534077Published at
Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '22) Boston, 9-13 July 2022. Pp. 15–16. New York: Association for Computing Machinery, 2022Publisher
ACMKeywords
cyber physical systems
Metamorphic Testing
Quality of Service
oracle generation ... [+]
Metamorphic Testing
Quality of Service
oracle generation ... [+]
cyber physical systems
Metamorphic Testing
Quality of Service
oracle generation
oracle improvement
evolutionary algorithm
genetic programming
mutation testing [-]
Metamorphic Testing
Quality of Service
oracle generation
oracle improvement
evolutionary algorithm
genetic programming
mutation testing [-]
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 ... [+]
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. [-]
