Requirements Engineering (RE) is a critical phase in the software development process that generates requirements specifications from stakeholders' needs. Recently, deep learning techniques have been successful in several RE tasks. However, obtaining high-quality requirements specifications requires collaboration across multiple tasks and roles. In this paper, we propose an innovative framework called MARE, which leverages collaboration among large language models (LLMs) throughout the entire RE process. MARE divides the RE process into four tasks: elicitation, modeling, verification, and specification. Each task is conducted by engaging one or two specific agents and each agent can conduct several actions. MARE has five agents and nine actions. To facilitate collaboration between agents, MARE has designed a workspace for agents to upload their generated intermediate requirements artifacts and obtain the information they need. We conduct experiments on five public cases, one dataset, and four new cases created by this work. We compared MARE with three baselines using three widely used metrics for the generated requirements models. Experimental results show that MARE can generate more correct requirements models and outperform the state-of-the-art approaches by 15.4%. For the generated requirements specifications, we conduct a human evaluation in three aspects and provide insights about the quality

翻译：需求工程（RE）是软件开发过程中的关键阶段，其目标是从利益相关者需求中生成需求规格说明。近年来，深度学习技术已在多项RE任务中取得成效。然而，获取高质量的需求规格说明需要跨任务与跨角色的协作。本文提出了一种创新框架MARE，该框架在整个RE流程中利用大语言模型（LLMs）间的协作。MARE将RE流程划分为四个任务：需求获取、建模、验证与规格说明。每个任务由一个或两个特定智能体执行，每个智能体可执行若干动作。MARE共包含五个智能体与九个动作。为促进智能体间协作，MARE设计了工作空间，使智能体能够上传生成的中介需求制品并获取所需信息。我们在五个公开案例、一个数据集及本工作创建的四个新案例上进行了实验。采用三种广泛使用的指标将MARE与三个基线方法生成的需求模型进行对比。实验结果表明，MARE能生成更正确的需求模型，性能较当前最优方法提升15.4%。对于生成的需求规格说明，我们通过三个维度的人工评估，深入分析了其质量特征。