标题： 像工程师一样思考：用于生成软件需求获取和自检的神经符号协作代理 显示英文标题

标题： Think Like an Engineer: A Neuro-Symbolic Collaboration Agent for Generative Software Requirements Elicitation and Self-Review

摘要： 端用户软件工程（EUSE）的愿景是赋予非专业用户对软件开发生命周期的完全控制权。 它的目标是使用户仅使用自然语言需求就能驱动生成式软件开发。 然而，由于终端用户通常缺乏软件工程知识，他们的需求描述经常是模糊的，这对生成式软件开发提出了重大挑战。 尽管现有方法利用Gherkin等结构化语言来澄清用户叙述，但它们仍然难以表达前提条件和行为动作之间的因果逻辑。 本文介绍了RequireCEG，这是一种需求获取和自检代理，它在神经符号协作架构中嵌入了因果图（CEGs）。 RequireCEG首先使用特征树对用户叙述进行分层分析，明确界定软件组件的范围及其系统行为需求。 接下来，它根据获取的需求构建自我修复的CEGs，捕捉原子前提条件和行为动作之间的因果关系。 最后，构建的CEGs用于审查和优化Gherkin场景，确保生成的Gherkin需求与从用户叙述中获取的系统行为需求之间的一致性。 为了评估我们的方法，我们创建了RGPair基准数据集并进行了大量实验。 它实现了87%的覆盖率，并将多样性提高了51.88%。 显示英文摘要

摘要： The vision of End-User Software Engineering (EUSE) is to empower non-professional users with full control over the software development lifecycle. It aims to enable users to drive generative software development using only natural language requirements. However, since end-users often lack knowledge of software engineering, their requirement descriptions are frequently ambiguous, raising significant challenges to generative software development. Although existing approaches utilize structured languages like Gherkin to clarify user narratives, they still struggle to express the causal logic between preconditions and behavior actions. This paper introduces RequireCEG, a requirement elicitation and self-review agent that embeds causal-effect graphs (CEGs) in a neuro-symbolic collaboration architecture. RequireCEG first uses a feature tree to analyze user narratives hierarchically, clearly defining the scope of software components and their system behavior requirements. Next, it constructs the self-healing CEGs based on the elicited requirements, capturing the causal relationships between atomic preconditions and behavioral actions. Finally, the constructed CEGs are used to review and optimize Gherkin scenarios, ensuring consistency between the generated Gherkin requirements and the system behavior requirements elicited from user narratives. To evaluate our method, we created the RGPair benchmark dataset and conducted extensive experiments. It achieves an 87% coverage rate and raises diversity by 51.88%.