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ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Software
Volume 7 - 2025 |
doi: 10.3389/fcomp.2025.1537100
This article is part of the Research Topic Machine Learning for Software Engineering View all 3 articles
Automated Requirements Engineering Framework in Agile Model-Driven Development
Provisionally accepted- 1 King's College London, London, United Kingdom
- 2 Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences, King's College London, London, England, United Kingdom
- 3 Department of Computer Science, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, United Kingdom
The advances in requirements engineering, marked by various paradigms and methodologies, have substantially impacted software development practices. One such is the integration of agile methodologies and model-driven development, which has become increasingly essential and critical to model-driven engineering (MDE). The core principle of MDE is the application of models at various stages of software development. Thus, this paper proposes an automated requirements engineering framework in agile model-driven development to enhance the formalization and analysis of requirements written in natural language. The framework leverages Machine Learning models to extract essential components from requirements specifications, specifically focusing on class diagrams. A comprehensive dataset of requirements specification problems was developed to train and validate the proposed framework's efficacy. Two real-world industrial case study evaluations were conducted, featuring the framework's application in medical and information systems domains. These case studies provided valuable insights into the framework's applicability and practicality within diverse and complex software development environments. The findings contribute not only to automated requirements engineering but also to the broad area of agile Model-Driven Development.
Keywords: requirements engineering, Model-driven engineering, Model-driven development, Agile development, machine learning, nlp
Received: 29 Nov 2024; Accepted: 27 Jan 2025.
Copyright: © 2025 Umar, Lano and Abubakar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Muhammad Aminu Umar, King's College London, London, United Kingdom
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