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Constrained adversarial learning for automated software testing: a literature review

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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorVitorino, João
dc.contributor.authorMachado Vitorino, João Pedro
dc.contributor.authorDias, Tiago
dc.contributor.authorDias, Tiago Fontes
dc.contributor.authorFonseca, Tiago
dc.contributor.authorCaló Fonseca, Tiago Carlos
dc.contributor.authorMaia, Eva
dc.contributor.authorMaia, Eva
dc.contributor.authorPraça, Isabel
dc.contributor.authorPraça, Isabel
dc.date.accessioned2026-05-27T10:51:51Z
dc.date.available2026-05-27T10:51:51Z
dc.date.issued2025
dc.description.abstractIt is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing can be a promising solution to quickly analyze many lines of code and detect vulnerabilities and possible attack vectors by generating function-specific testing data. This process draws similarities to the constrained adversarial examples generated by adversarial learning methods, so there could be significant benefits to the integration of these methods in testing tools. Therefore, this literature review is focused on the current state-of-the-art of constrained data generation methods applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance software testing tools with adversarial testing methods and improve the resilience and robustness of their information systems. The found constrained data generation applications were systematized, and the advantages and limitations of approaches specific for white-box, grey-box, and black-box testing were analyzed, identifying research gaps and opportunities to improve automated testing tools with data generated by adversarial attacks.eng
dc.identifier.citationVitorino, J., Dias, T., Fonseca, T. et al. Constrained adversarial learning for automated software testing: a literature review. Discov Appl Sci 7, 547 (2025). https://doi.org/10.1007/s42452-025-07073-3
dc.identifier.doi10.1007/s42452-025-07073-3
dc.identifier.issn3004-9261
dc.identifier.urihttp://hdl.handle.net/10400.22/32432
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.hasversionhttps://link.springer.com/article/10.1007/s42452-025-07073-3#citeas
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSoftware development
dc.subjectAdversarial testing
dc.subjectAdversarial attack
dc.subjectConstrained data generation
dc.subjectMachine learning
dc.titleConstrained adversarial learning for automated software testing: a literature revieweng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/00760/2020
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PT
oaire.citation.issue547
oaire.citation.titleDiscover Applied Sciences
oaire.citation.volume7
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMachado Vitorino
person.familyNameDias
person.familyNameCaló Fonseca
person.familyNameMaia
person.familyNamePraça
person.givenNameJoão Pedro
person.givenNameTiago Fontes
person.givenNameTiago Carlos
person.givenNameEva
person.givenNameIsabel
person.identifierHlqCxhoAAAAJ
person.identifier2140741
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person.identifier.ciencia-id3312-592F-B628
person.identifier.ciencia-idF510-7CAD-42CD
person.identifier.ciencia-id9210-B002-2CB5
person.identifier.ciencia-id4F14-EF83-C4B9
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.orcid0000-0002-4968-3653
person.identifier.orcid0000-0002-1693-7872
person.identifier.orcid0000-0002-5592-3107
person.identifier.orcid0000-0002-8075-531X
person.identifier.orcid0000-0002-2519-9859
person.identifier.ridK-8430-2014
person.identifier.scopus-author-id57579914600
person.identifier.scopus-author-id57355096600
person.identifier.scopus-author-id22734900800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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