Large Language Models (LLMs) for Requirements Engineering (RE): A Systematic Literature Review

• URL: http://arxiv.org/abs/2509.11446v1
• Date: Sun, 14 Sep 2025 21:45:01 GMT
• Title: Large Language Models (LLMs) for Requirements Engineering (RE): A Systematic Literature Review
• Authors: Mohammad Amin Zadenoori, Jacek Dąbrowski, Waad Alhoshan, Liping Zhao, Alessio Ferrari,
• Abstract summary: The study categorizes the literature according to several dimensions, including publication trends, RE activities, prompting strategies, and evaluation methods.<n>Most of the studies focus on using LLMs for requirements elicitation and validation, rather than defect detection and classification.<n>Other artifacts are increasingly considered, including issues from issue tracking systems, regulations, and technical manuals.
• Score: 2.0061679654181392
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) are finding applications in numerous domains, and Requirements Engineering (RE) is increasingly benefiting from their capabilities to assist with complex, language-intensive tasks. This paper presents a systematic literature review of 74 primary studies published between 2023 and 2024, examining how LLMs are being applied in RE. The study categorizes the literature according to several dimensions, including publication trends, RE activities, prompting strategies, and evaluation methods. Our findings indicate notable patterns, among which we observe substantial differences compared to previous works leveraging standard Natural Language Processing (NLP) techniques. Most of the studies focus on using LLMs for requirements elicitation and validation, rather than defect detection and classification, which were dominant in the past. Researchers have also broadened their focus and addressed novel tasks, e.g., test generation, exploring the integration of RE with other software engineering (SE) disciplines. Although requirements specifications remain the primary focus, other artifacts are increasingly considered, including issues from issue tracking systems, regulations, and technical manuals. The studies mostly rely on GPT-based models, and often use Zero-shot or Few-shot prompting. They are usually evaluated in controlled environments, with limited use in industry settings and limited integration in complex workflows. Our study outlines important future directions, such as leveraging the potential to expand the influence of RE in SE, exploring less-studied tasks, improving prompting methods, and testing in real-world environments. Our contribution also helps researchers and practitioners use LLMs more effectively in RE, by providing a list of identified tools leveraging LLMs for RE, as well as datasets.

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