Systematic mapping of non-functional requirements and their impacts in architectures for artificial intelligence

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cic.institucionOrigenLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.isFulltextSI
cic.isPeerReviewedSI
cic.lugarDesarrolloLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.parentTypeArtículo
cic.versionPublicada
dc.date.accessioned2026-03-03T15:57:20Z
dc.date.available2026-03-03T15:57:20Z
dc.identifier.urihttps://digital.cic.gba.gob.ar/handle/11746/12658
dc.titleSystematic mapping of non-functional requirements and their impacts in architectures for artificial intelligenceen
dc.typeArtículo
dcterms.abstractArtificial intelligence (AI) is a branch of computer science that seeks algorithms capable of learning, reasoning, adapting, and performing tasks in a human-like manner. Software is determined by a set of functional requirements (FRs) and non-functional requirements (NFRs). NFRs, such as scalability, security, performance, and usability, define how a system should function beyond its basic functionalities. The architecture must be designed to meet these NFRs, ensuring system efficiency and reliability while maintaining a flexible and modularstructure.Project-specific requirements, technological advancements, and the domain greatly influence the variety of NFRs and architectures that will be essential for the development of AI-based systems. Selecting an appropriate architecture is a processintrinsically guided by the prioritized NFRs. This systematic mapping seeks to provide the NFRs, architectures, and how these impact the selection of architectures for AI-based systemsand a taxonomy. Theevidence gathered shows that NFRs tend to prioritize security, scalability, and accuracy. At the architectural level, there is a similar trend toward patterns such as microservices and lambda. Finally, in terms of impact, microservices is highly influenced by reliability and scalability, workflow orchestration by transparency and explainability, and federated learning by sensitive data security.en
dcterms.creator.authorAstaiza, Yefry
dcterms.creator.authorLópez Erazo, Oscar Santiago
dcterms.creator.authorMuñoz, Luis Freddy
dcterms.creator.authorDelle Ville, Juliana
dcterms.creator.authorMaltempo, Giuliana
dcterms.creator.authorAntonelli, Leandro
dcterms.creator.authorHurtado, Julio Ariel
dcterms.creator.authorCollazos, Cesar
dcterms.creator.authorRuiz, Pablo
dcterms.creator.authorAgredo, Vanessa
dcterms.identifier.otherDOI: 10.19153/cleiej.29.1.2
dcterms.identifier.otherISSN: 0717-5000
dcterms.identifier.urlhttps://doi.org/10.19153/cleiej.29.1.2
dcterms.isPartOf.issuevol. 29, no. 1
dcterms.isPartOf.seriesCLEI Electronic Journal
dcterms.issued2026-02
dcterms.languageInglés
dcterms.licenseAttribution 4.0 International (BY 4.0)
dcterms.subjectArtificial Intelligenceen
dcterms.subjectNon-functional requirementsen
dcterms.subjectSoftware Architectureen
dcterms.subjectSystematic Mappingen
dcterms.subjectImpacten
dcterms.subject.materiaCiencias de la Computación e Información

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