Registro sencillo

dc.contributor.authorAyerdi, Jon
dc.contributor.authorArrieta, Aitor
dc.contributor.authorSagardui, Goiuria
dc.contributor.otherTerragni, Valerio
dc.contributor.otherTonella, Paolo
dc.contributor.otherArratibel, Maite
dc.date.accessioned2022-12-07T08:36:50Z
dc.date.available2022-12-07T08:36:50Z
dc.date.issued2022
dc.identifier.isbn978-1-4503-9268-6en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=169003en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5907
dc.description.abstractA 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.en
dc.description.sponsorshipComisión Europeaes
dc.description.sponsorshipGobierno Vasco-Eusko Jaurlaritzaes
dc.language.isoengen
dc.publisherACMen
dc.rights© 2022 The Authorsen
dc.subjectcyber physical systemsen
dc.subjectMetamorphic Testingen
dc.subjectQuality of Serviceen
dc.subjectoracle generationen
dc.subjectoracle improvementen
dc.subjectevolutionary algorithmen
dc.subjectgenetic programmingen
dc.subjectmutation testingen
dc.titleEvolutionary generation of metamorphic relations for cyber-physical systemses
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceProceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '22)en
local.contributor.groupIngeniería del software y sistemases
local.description.peerreviewedtrueen
local.description.publicationfirstpage15en
local.description.publicationlastpage16en
local.identifier.doihttps://doi.org/10.1145/3520304.3534077en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/871319/EU/Design-Operation Continuum Methods for Testing and Deployment under Unforeseen Conditions for Cyber-Physical Systems of Systems/ADEPTNESSen
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020. ERC Advanced Grant 2017 Program/787703/EU/Self-assessment Oracles for Anticipatory Testing/PRECRIMEen
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Ikertalde Convocatoria 2022-2023/IT1519-22/CAPV/Ingeniería de Software y Sistemas/en
local.contributor.otherinstitutionhttps://ror.org/03b94tp07en
local.contributor.otherinstitutionhttps://ror.org/03c4atk17es
local.contributor.otherinstitutionOrona S.Coop.es
local.source.detailsBoston, 9-13 July 2022. Pp. 15–16. New York: Association for Computing Machinery, 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


Ficheros en el ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(es)

Registro sencillo