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Towards a FAIR Dataset for Spanish Non-Functional Requirements
dc.contributor.author | Limaylla-Lunarejo, María-Isabel | |
dc.contributor.author | Condori Fernández, Nelly | |
dc.contributor.author | Rodríguez Luaces, Miguel | |
dc.date.accessioned | 2023-11-10T19:12:54Z | |
dc.date.available | 2023-11-10T19:12:54Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | http://hdl.handle.net/2183/34160 | |
dc.description | Cursos e Congresos, C-155 | es_ES |
dc.description.abstract | [Abstract] Supervised Machine Learning algorithms (ML) have enhanced the performance of the automatic non-functional requirements (NFR) classification in the Requirements Engineering domain. However, the lack of public datasets, dealing with imbalanced datasets and reproducibility are current concerns in ML experiments. We conducted a quasi-experiment to generate a dataset of NFR in the Spanish Language, following the FAIR Principles. We collected 109 requirements from an open access repository of the University of A Coru˜ na, and performed a labeling process based in the categories and subcategories of the ISO/IEC 25010 quality model. Using a Fleiss’ Kappa test we obtained a substantial agreement (0.78) at the category level and a moderate agreement (0.48) when the classification is per subcategory supervised Machine Learning algorithms (ML) have enhanced the performance of the automatic non-functional requirements (NFR) classification in the Requirements Engineering domain. However, the lack of public datasets, dealing with imbalanced datasets and reproducibility are current concerns in ML experiments. We conducted a quasi-experiment to generate a dataset of NFR in the Spanish Language, following the FAIR Principles. We collected 109 requirements from an open access repository of the University of A Coruña, and performed a labeling process based in the categories and subcategories of the ISO/IEC 25010 quality model. Using a Fleiss’ Kappa test we obtained a substantial agreement (0.78) at the category level and a moderate agreement (0.48) when the classification is per subcategory | es_ES |
dc.description.sponsorship | CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Conseller ´ıa de Cultura, Educaci´on, Formaci´on Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS) | |
dc.language.iso | eng | es_ES |
dc.publisher | Universidade da Coruña, Servizo de Publicacións | es_ES |
dc.relation.uri | https://doi.org/10.17979/spudc.000024.30 | |
dc.rights | Attribution 4.0 International (CC BY 4.0) | es_ES |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/deed.es | * |
dc.subject | Aprendizaje automático | es_ES |
dc.subject | Principios FAIR | es_ES |
dc.subject | Kappa de Fleiss | es_ES |
dc.title | Towards a FAIR Dataset for Spanish Non-Functional Requirements | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.startPage | 191 | es_ES |
UDC.endPage | 196 | es_ES |
UDC.conferenceTitle | VI Congreso Xove TIC: impulsando el talento científico. Octubre, 2023, A Coruña | es_ES |