A Framework for Improving Software Requirement Prioritization Using Hierarchical Cumulative Voting (HCV) and Best-Worst Method (BWM) in Agile Project

Abstract

Requirements prioritization is a fundamental activity in requirements engineering, as it directly impacts release planning, resource allocation, and project success. In agile software development (ASD), this process becomes more challenging due to evolving requirements, multiple stakeholders, limited scalability, and the high cognitive burden of traditional techniques. This study proposes a hybrid prioritization framework that combines the hierarchical cumulative voting (HCV), used to structure requirements and simplify stakeholder input, with the best-worst method (BWM), used to determine consistent expert weights with fewer pairwise comparisons. The stakeholder and expert rankings were integrated using reciprocal rank fusion (RRF) to produce a unified ranking. The framework was evaluated through a case study of an Organizational Quality Management System (OQMS), which included 17 requirements, 43 stakeholders, and 10 experts. The results showed that the approach was effective and reliable, demonstrating that the proposed framework reduced the effort required for requirements prioritization while maintaining a high level of judgment consistency and producing a reliable prioritization result. Overall, the framework demonstrated effectiveness and scalability for ASD while preserving the perspectives of stakeholders and experts.