Aspect‐based requirements mining technique to improve prioritisation process: multi‐stakeholder perspective. Issue 5 (1st October 2020)

• Record Type: Journal Article
• Authors: Ali, Sadia; Hafeez, Yaser; Asghar, Sohail; Nawaz, Asif; Saeed, Saqib
• Is Part Of: IET software. Volume 14:Issue 5(2020)
• Page Start: 482
• Page End: 492
• Publication Date: 2020-10-01
• Subjects: software development management -- systems analysis -- formal specification -- data mining -- feature extraction -- decision making; development teams -- requirement prioritisation techniques -- multistakeholder perspective -- optimal prioritisation process -- release planning -- software development project -- requirement prioritisation process -- text mining -- clustering techniques -- multistakeholder context -- analytical heretical process; Computer software -- Periodicals; Software engineering -- Periodicals; 005.1
• DOI: 10.1049/iet-sen.2019.0332
• Languages: English
• ISSNs: 1751-8806

Abstract

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.