Enhancing Agile Requirements Change Management: Integrating LLMs with Fuzzy Best-Worst Method for Decision Support

Agile Requirements Change Management (ARCM) in Global Software Development (GSD) posed significant challenges due to the dynamic nature of project requirements and the complexities of distributed team coordination. One approach used to mitigate these challenges and ensure efficient collaboration is the identification and prioritization of success factors. Traditional Multi-Criteria Decision-Making methods, such as the Best-Worst Method (BWM), had been employed successfully to prioritize success factors. However, these methods often failed to capture the inherent uncertainties of decision-making in a GSD. To address this limitation, this study integrated Large Language Models (LLMs) with the Fuzzy Best-Worst Method (FBWM) to enhance prioritization accuracy and decision support. We propose a model for comparing the prioritization outcomes of human expert assessments and LLM-generated decisions to evaluate the consistency and effectiveness of machine-generated decisions relative to those made by human experts. The findings indicate that the LLM-driven FBWM exhibit high reliability in mirroring expert judgments, demonstrating the potential of LLMs to support strategic decision-making in ARCM. This study contributed to the evolving landscape of AI-driven project management by providing empirical evidence of LLMs’ utility in improving ARCM for GSD.