探索LLMs用于从mockups中提取用户故事
Exploring LLMs for User Story Extraction from Mockups
AI总结：
本文探讨了利用LLMs结合LEL从mockups中自动提取用户故事，提升了生成准确性与适用性，推动AI在需求工程中的应用。
摘要：
用户故事是软件行业中最广泛使用的 artifacts 之一，用于定义功能性需求。同时，高保真mockups的使用有助于终端用户参与定义他们的需求。在本工作中，我们探索如何将这些技术与大型语言模型（LLMs）结合，实现敏捷和自动化的用户故事生成。为此，我们呈现了一个案例研究，分析LLMs从高保真mockups中提取用户故事的能力，包括和不包括语言扩展词典（LEL）在提示中的情况。我们的结果表明，结合LEL显著提高了生成用户故事的准确性和适用性。这种方法代表了AI在需求工程整合中的一步进展，有潜力改善用户与开发人员之间的沟通。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
