Zobrazit v EDS

A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based Projects

Uloženo v:
Podrobná bibliografie
Název: A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based Projects
Autoři: Felipe Ramos, Alexandre Costa, Mirko Perkusich, Luiz Silva, Dalton Valadares, Ademar de Sousa Neto, Felipe Cunha, Hyggo Almeida, Angelo Perkusich
Zdroj: IEEE Access, Vol 13, Pp 44000-44023 (2025)
Informace o vydavateli: Institute of Electrical and Electronics Engineers (IEEE), 2025.
Rok vydání: 2025
Témata: intelligent systems, scrum framework, Electrical engineering. Electronics. Nuclear engineering, NFR elicitation, data-driven recommendation, Agile development, TK1-9971
Popis: Context: Agile software development, particularly Scrum, enables teams to manage evolving requirements by emphasizing face-to-face communication and incremental deliveries. Although effective in addressing functional requirements, agile methods often overlook non-functional requirements during the initial stages of software projects, potentially leading to cost overruns on software and hardware and project failures exceeding 60%. Objective: In this article, we introduce a data-driven recommendation system to assist Scrum teams in eliciting NFRs effectively and early in the development lifecycle. Method: Our proposed solution applies the k-nearest neighbors algorithm to recommend non-functional requirements by leveraging historical project data structured through a taxonomy of user stories. We evaluated the system through offline experiments under the cross-validation protocol, utilizing datasets from 13 real-world projects. Results: Our recommendation system achieved an F-measure of up to 79%, demonstrating its ability to provide accurate and context-aware non-functional requirements suggestions. Conclusion: These findings suggest that our solution supports agile teams by automating non-functional requirement elicitation and enhancing decision-making processes, thereby addressing critical gaps in non-functional requirement integration within Scrum-based projects.
Druh dokumentu: Article
ISSN: 2169-3536
DOI: 10.1109/access.2025.3548631
Přístupová URL adresa: https://doaj.org/article/859c0484b23e4fac8f5e5219e698ca12
Rights: CC BY
Přístupové číslo: edsair.doi.dedup.....7e5f2defd6e6e307b03fec30ffdd5cd1
Databáze: OpenAIRE
Popis
Abstrakt:Context: Agile software development, particularly Scrum, enables teams to manage evolving requirements by emphasizing face-to-face communication and incremental deliveries. Although effective in addressing functional requirements, agile methods often overlook non-functional requirements during the initial stages of software projects, potentially leading to cost overruns on software and hardware and project failures exceeding 60%. Objective: In this article, we introduce a data-driven recommendation system to assist Scrum teams in eliciting NFRs effectively and early in the development lifecycle. Method: Our proposed solution applies the k-nearest neighbors algorithm to recommend non-functional requirements by leveraging historical project data structured through a taxonomy of user stories. We evaluated the system through offline experiments under the cross-validation protocol, utilizing datasets from 13 real-world projects. Results: Our recommendation system achieved an F-measure of up to 79%, demonstrating its ability to provide accurate and context-aware non-functional requirements suggestions. Conclusion: These findings suggest that our solution supports agile teams by automating non-functional requirement elicitation and enhancing decision-making processes, thereby addressing critical gaps in non-functional requirement integration within Scrum-based projects.
ISSN:21693536
DOI:10.1109/access.2025.3548631