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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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10915619/ |
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| Summary: | <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. |
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| ISSN: | 2169-3536 |