Abstract
Requirement prioritisation and selection is an essential activity in modern‐day large software development. Optimal prioritisation process is critical for successful implementation and release planning in a software development project. Requirement prioritisation becomes more challenging in projects having large sets of requirements and stakeholders, having diverse perspectives resulting in irrelevancy and ambiguity during features extraction. This study aims to improve requirement prioritisation process using text mining and clustering techniques for accurate extraction of features and requirement prioritisation in multi‐stakeholder context. The proposed framework developed to avoid incompleteness in requirements and disagreement among development teams and stakeholders. Thus, the proposed framework compared with other requirement prioritisation techniques (i.e. Analytical Heretical Process, Commutative Voting and Wiegers) to highlight the significance of the proposed framework while conducting an experimental study. The results show that the proposed framework outperformed the traditional techniques and enhanced the prioritisation process with complete semantic information of extracted features and taking into account the diverse perspective of stakeholders.