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[2602.16997] Exploring LLMs for User Story Extraction from Mockups
Summary
This article explores the use of large language models (LLMs) for extracting user stories from high-fidelity mockups, enhancing requirements engineering in software development.
Why It Matters
As software development increasingly relies on user-centered design, automating user story extraction can streamline communication between stakeholders and improve the accuracy of functional requirements. This research highlights the potential of LLMs to enhance this process, making it more efficient and effective.
Key Takeaways
- LLMs can automate the generation of user stories from mockups.
- Incorporating a glossary significantly improves the accuracy of extracted user stories.
- This approach enhances communication between users and developers.
- The study presents a case study validating the effectiveness of LLMs in requirements engineering.
- The findings contribute to the integration of AI in software development practices.
Computer Science > Software Engineering arXiv:2602.16997 (cs) [Submitted on 19 Feb 2026] Title:Exploring LLMs for User Story Extraction from Mockups Authors:Diego Firmenich, Leandro Antonelli, Bruno Pazos, Fabricio Lozada, Leonardo Morales View a PDF of the paper titled Exploring LLMs for User Story Extraction from Mockups, by Diego Firmenich and 4 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) ACM classes: D.2.1; I.2.7; D.2.2 Cite as: arXiv:2602.16997 [cs.SE]...
