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ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Software
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1519437
This article is part of the Research Topic Machine Learning for Software Engineering View all 4 articles
Research Directions for Using LLM in Software Requirement Engineering: A Systematic Review
Provisionally accepted- 1 University of Isfahan, Isfahan, Isfahan, Iran
- 2 University of Roehampton London, Roehampton, London, United Kingdom
- 3 King's College London, London, England, United Kingdom
- 4 University College London, London, England, United Kingdom
Natural Language Processing (NLP) and Large Language Models (LLMs) are transforming the landscape of software engineering, especially in the domain of requirement engineering. Despite significant advancements, there is a notable lack of comprehensive survey papers that provide a holistic view of the impact of these technologies on requirement engineering. This paper addresses this gap by reviewing the current state of NLP and LLMs in requirement engineering, highlighting their effects on improving requirement extraction, analysis, and specification. We analyze trends in software requirement engineering papers, noting an upward trajectory in the application of LLMs in software engineering tasks. This review underscores the critical role of requirement engineering in the software development lifecycle and emphasizes the transformative potential of LLMs in enhancing precision and reducing ambiguities in requirement specifications.Our findings indicate a growing interest and significant progress in leveraging LLMs for various software engineering tasks, particularly in requirement engineering. This paper aims to provide a foundation for future research and identify key challenges and opportunities in this evolving field.
Keywords: Software Development, Requirement engineering, Large Language Models (LLMs), Systematic Literature Review, Requirement specification
Received: 29 Oct 2024; Accepted: 28 Feb 2025.
Copyright: © 2025 Hemmat, Sharbaf, Rahimi, Lano and Y. Tehrani. 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:
Sobhan Y. Tehrani, University College London, London, WC1E 6BT, England, United Kingdom
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