Enhancing Agile Software Development: A Novel Approach to Automated Requirements Prioritization

In Agile Software Development (ASD), accurately comprehending and prioritizing project requirements is crucial for aligning project results with stakeholder expectations and achieving success. The “Rule-Based Automated Requirements Prioritization” (RAR-P) paradigm is novel in using Natural Language Processing (NLP) and Machine Learning (ML) to improve requirements prioritization (RP). The RAR-P model uses advanced text preprocessing, tokenization, TF - ID F vectorization, cosine similarity measures, log likelihood calculations, and K-Means clustering to systematically analyze and priorities user requirements based on their semantic importance. This innovative approach surpasses the constraints of conventional prioritization methods by automating the decision-making process, guaranteeing a more profound and impartial alignment of project outcomes with the subtle requirements of stakeholders. This article explores the creation and use of the RAR- P model, demonstrating its ability to greatly improve the efficiency and efficacy of requirement prioritization in ASD. The RAR-P model has been identified as a crucial tool for transforming software development projects, offering the potential for improved success rates and more efficient resource allocation in project management. This study introduces an innovative solution to a persistent problem and also creates opportunities for future developments in agile project management approaches.