A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based Projects
<bold>Context:</bold> 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...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10915619/ |
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| author | Felipe Ramos Alexandre Costa Mirko Perkusich Luiz Silva Dalton Valadares Ademar de Sousa Neto Felipe Cunha Hyggo Almeida Angelo Perkusich |
| author_facet | Felipe Ramos Alexandre Costa Mirko Perkusich Luiz Silva Dalton Valadares Ademar de Sousa Neto Felipe Cunha Hyggo Almeida Angelo Perkusich |
| author_sort | Felipe Ramos |
| collection | DOAJ |
| description | <bold>Context:</bold> 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%. <bold>Objective:</bold> In this article, we introduce a data-driven recommendation system to assist Scrum teams in eliciting NFRs effectively and early in the development lifecycle. <bold>Method:</bold> 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. <bold>Results:</bold> Our recommendation system achieved an F-measure of up to 79%, demonstrating its ability to provide accurate and context-aware non-functional requirements suggestions. <bold>Conclusion:</bold> 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. |
| format | Article |
| id | doaj-art-859c0484b23e4fac8f5e5219e698ca12 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-859c0484b23e4fac8f5e5219e698ca122025-08-20T03:01:31ZengIEEEIEEE Access2169-35362025-01-0113440004402310.1109/ACCESS.2025.354863110915619A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based ProjectsFelipe Ramos0https://orcid.org/0000-0002-0937-811XAlexandre Costa1https://orcid.org/0000-0002-2258-5201Mirko Perkusich2https://orcid.org/0000-0002-9433-4962Luiz Silva3https://orcid.org/0000-0001-5803-2636Dalton Valadares4Ademar de Sousa Neto5https://orcid.org/0000-0002-1651-4159Felipe Cunha6https://orcid.org/0000-0001-6836-7560Hyggo Almeida7Angelo Perkusich8https://orcid.org/0000-0002-7377-1258Intelligent Software Engineering Group (ISE/VIRTUS), Federal University of Campina Grande, Campina Grande, BrazilIntelligent Software Engineering Group (ISE/VIRTUS), Federal University of Campina Grande, Campina Grande, BrazilIntelligent Software Engineering Group (ISE/VIRTUS), Federal University of Campina Grande, Campina Grande, BrazilIntelligent Software Engineering Group (ISE/VIRTUS), Federal University of Campina Grande, Campina Grande, BrazilIntelligent Software Engineering Group (ISE/VIRTUS), Federal University of Campina Grande, Campina Grande, BrazilIntelligent Software Engineering Group (ISE/VIRTUS), Federal University of Campina Grande, Campina Grande, BrazilIntelligent Software Engineering Group (ISE/VIRTUS), Federal University of Campina Grande, Campina Grande, BrazilIntelligent Software Engineering Group (ISE/VIRTUS), Federal University of Campina Grande, Campina Grande, BrazilIntelligent Software Engineering Group (ISE/VIRTUS), Federal University of Campina Grande, Campina Grande, Brazil<bold>Context:</bold> 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%. <bold>Objective:</bold> In this article, we introduce a data-driven recommendation system to assist Scrum teams in eliciting NFRs effectively and early in the development lifecycle. <bold>Method:</bold> 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. <bold>Results:</bold> Our recommendation system achieved an F-measure of up to 79%, demonstrating its ability to provide accurate and context-aware non-functional requirements suggestions. <bold>Conclusion:</bold> 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.https://ieeexplore.ieee.org/document/10915619/Agile developmentNFR elicitationdata-driven recommendationscrum frameworkintelligent systems |
| spellingShingle | Felipe Ramos Alexandre Costa Mirko Perkusich Luiz Silva Dalton Valadares Ademar de Sousa Neto Felipe Cunha Hyggo Almeida Angelo Perkusich A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based Projects IEEE Access Agile development NFR elicitation data-driven recommendation scrum framework intelligent systems |
| title | A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based Projects |
| title_full | A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based Projects |
| title_fullStr | A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based Projects |
| title_full_unstemmed | A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based Projects |
| title_short | A Data-Driven Recommendation System for Enhancing Non-Functional Requirements Elicitation in Scrum-Based Projects |
| title_sort | data driven recommendation system for enhancing non functional requirements elicitation in scrum based projects |
| topic | Agile development NFR elicitation data-driven recommendation scrum framework intelligent systems |
| url | https://ieeexplore.ieee.org/document/10915619/ |
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