Exploring LLMs for User Story Extraction from Mockups
This work addresses the challenge of improving communication between users and developers in requirements engineering, though it is incremental as it builds on existing techniques with LLMs.
The paper tackled the problem of automating user story generation from high-fidelity mockups in software requirements engineering, and found that incorporating a Language Extended Lexicon glossary into prompts significantly enhanced the accuracy and suitability of the stories generated by large language models.
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.