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.

翻译：用户故事是软件行业定义功能需求时最广泛使用的工件之一。与此同时，高保真设计原型的使用有助于最终用户参与定义其需求。本研究探讨了如何将这些技术与大语言模型相结合，实现从设计原型中敏捷且自动化地生成用户故事。为此，我们通过一项案例研究，分析了大语言模型从高保真设计原型中提取用户故事的能力，包括在提示中是否包含语言扩展词典术语表两种情况。实验结果表明，引入语言扩展词典能显著提升生成用户故事的准确性与适用性。该方法代表了人工智能与需求工程融合的重要进展，有望改善用户与开发者之间的沟通效率。