ReqInOne: A Large Language Model-Based Agent for Software Requirements Specification Generation
Zhu, Taohong Cordeiro, Lucas Carvalho Sun, Youcheng
Zhu, Taohong
Cordeiro, Lucas Carvalho
Sun, Youcheng
Supervisor
Department
Computer Science
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Abstract
Software Requirements Specification (SRS) is one of the most important documents in software projects, but writing it manually is time-consuming and often leads to ambiguity. Existing automatic methods rely heavily on manual analysis, while recent Large Language Model (LLM)-based approaches suffer from hallucinations and poor controllability. In this paper, we propose ReqInOne, an LLM-based agent that follows the common steps taken by human requirements engineers when writing an SRS to convert natural language into a structured SRS. ReqInOne features a modular architecture by decomposing SRS generation into three tasks: summary, requirement extraction, and requirement classification, each supported by tailored prompt templates to improve the quality and consistency of LLM outputs.We evaluate ReqInOne using GPT-4o, LLaMA 3, and DeepSeek-R1, and compare the generated SRSs against those produced by the holistic GPT-4-based SRS generation method from existing work as well as by entry-level requirements engineers. Expert evaluations show that ReqInOne produces more accurate and well-structured SRS documents. The performance advantage of ReqInOne benefits from its modular design, and experimental results further demonstrate that its requirement classification component achieves comparable or even better results than the state-of-the-art requirement classification model.
Citation
T. Zhu, L. C. Cordeiro and Y. Sun, "ReqInOne: A Large Language Model-Based Agent for Software Requirements Specification Generation," 2025 IEEE 33rd International Requirements Engineering Conference (RE), Valencia, Spain, 2025, pp. 449-457, doi: 10.1109/RE63999.2025.00054.
Source
Proceedings of the IEEE International Conference on Requirements Engineering
Conference
33rd IEEE International Requirements Engineering Conference, RE 2025
Keywords
Large Language Model, Requirements engineering, Software Requirements Specification
Subjects
Source
33rd IEEE International Requirements Engineering Conference, RE 2025
Publisher
IEEE