AUTHOR=Hemmat Arshia , Sharbaf Mohammadreza , Kolahdouz-Rahimi Shekoufeh , Lano Kevin , Tehrani Sobhan Y. 

TITLE=Research directions for using LLM in software requirement engineering: a systematic review

JOURNAL=Frontiers in Computer Science

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1519437

DOI=10.3389/fcomp.2025.1519437

ISSN=2624-9898

ABSTRACT=IntroductionNatural 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.MethodsWe analyze trends in software requirement engineering papers, focusing on the application of NLP and LLMs. The review highlights their effects on improving requirement extraction, analysis, and specification, and identifies key patterns in the adoption of these technologies.ResultsThe findings reveal an upward trajectory in the use of LLMs for software engineering tasks, particularly in requirement engineering. The 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.DiscussionThis paper identifies a growing interest and significant progress in leveraging LLMs for various software engineering tasks, particularly in requirement engineering. It provides a foundation for future research and highlights key challenges and opportunities in this evolving field.