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Applying large language models to software develop-ment: enhancing requirements, design and code

2025-08-07Documento de conferência Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorSantos, Gonçalo
dc.contributor.authorSilveira, Clara
dc.contributor.authorSantos, Vitor
dc.contributor.authorSantos, Arnaldo
dc.contributor.authorSão Mamede, Henrique
dc.date.accessioned2026-01-09T14:18:06Z
dc.date.available2026-01-09T14:18:06Z
dc.date.issued2025-08-07
dc.description.abstractThis paper explores the potential of Large Language Models (LLM) to optimize various stages of the software development lifecycle, including require-ments elicitation, architecture design, diagram creation, and implementation. The study is grounded in a real-world case, where development time and result quality are compared with and without LLM assistance. This research underscores the possibility of applying prompt patterns in LLM to support and enhance software development activities, focusing on a B2C digital commerce platform centered on fashion retail, designated LUNA. The methodology adopted is Design Sci-ence, which follows a practical and iterative approach. Requirements, design sug-gestions, and code samples are analyzed before and after the application of lan-guage models. The results indicate substantial advantages in the development process, such as improved task efficiency, faster identification of requirement gaps, and enhanced code readability. Nevertheless, challenges were observed in interpreting complex business logic. Future work should explore the integration of LLM with domain-specific ontologies and business rule engines to improve contextual accuracy in code and model generation. Additionally, refining prompt engineering strategies and combining LLM with interactive development envi-ronments could further enhance code quality, traceability, and explainability.eng
dc.identifier.citationSantos, G., Silveira, C., Santos, V., Santos, A., & Mamede, H. (2025, July). Applying Large Language Models to Software Development: Enhancing Requirements, Design and Code. In International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence (pp. 226-239). Cham: Springer Nature Switzerland.
dc.identifier.doi10.1007/978-3-031-99474-6_20
dc.identifier.eissn2194-5365
dc.identifier.issn2194-5357
dc.identifier.urihttp://hdl.handle.net/10400.2/20740
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-031-99474-6_20
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computing ((AISC,volume 1465)); International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence
dc.rights.uriN/A
dc.subjectLarge Language Models
dc.subjectPrompt Engineering
dc.subjectSoftware Develop-ment Lifecycle
dc.titleApplying large language models to software develop-ment: enhancing requirements, design and codeeng
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage239
oaire.citation.startPage226
oaire.citation.titleNew Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence
oaire.citation.volume1465
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
person.familyNameSantos
person.familyNameSantos
person.familyNameSão Mamede
person.givenNameVitor
person.givenNameArnaldo
person.givenNameHenrique
person.identifierhttps://scholar.google.pt/citations?user=ik_0xPkAAAAJ&hl=pt-PT
person.identifierR-002-0P0
person.identifier.ciencia-id3211-438A-A256
person.identifier.ciencia-id7F17-9DAD-C007
person.identifier.orcid0000-0002-4223-7079
person.identifier.orcid0000-0001-5139-6728
person.identifier.orcid0000-0002-5383-9884
person.identifier.scopus-author-id36458782500
relation.isAuthorOfPublicationcfae0b83-1a4d-4309-b607-38f32441fbd2
relation.isAuthorOfPublicationb40e1515-a3da-429d-83f6-717be6d9f30d
relation.isAuthorOfPublication86fd6131-eed5-42be-9639-9466ddf680ab
relation.isAuthorOfPublication.latestForDiscovery86fd6131-eed5-42be-9639-9466ddf680ab

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