用大模型从高保真原型图自动提取用户故事，提升需求沟通效率。
Exploring LLMs for User Story Extraction from Mockups
- 将大模型与原型图结合，通过提示词注入术语词典提升提取效果。
- 加入LEL词典后，生成的用户故事准确率和适用性显著提升。
- 适合敏捷开发团队、需求分析师及希望自动化需求收集的项目方。
用户故事是软件行业中定义功能需求最广泛使用的文档之一。与此同时，高保真原型图有助于终端用户参与需求定义。本文探讨将这两者与大语言模型（LLMs）结合，实现从原型图中敏捷自动化生成用户故事。通过案例研究分析了LLMs在有无引入语言扩展词典（LEL）提示词的情况下，从高保真原型图中提取用户故事的能力。结果表明，引入LEL显著提升了生成用户故事的准确性和适用性。该方法推动了AI在需求工程中的集成，有望改善用户与开发者之间的沟通。
原文摘要 · Abstract (English)
User stories are one of the most widely used artifacts in the software industry to define functional requirements. In parallel, the use of high-fidelity mockups facilitates end-user participation in defining their needs. In this work, we explore how combining these techniques with large language models (LLMs) enables agile and automated generation of user stories from mockups. To this end, we present a case study that analyzes the ability of LLMs to extract user stories from high-fidelity mockups, both with and without the inclusion of a glossary of the Language Extended Lexicon (LEL) in the prompts. Our results demonstrate that incorporating the LEL significantly enhances the accuracy and suitability of the generated user stories. This approach represents a step forward in the integration of AI into requirements engineering, with the potential to improve communication between users and developers.
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