GUIDE: LLM-Driven GUI Generation Decomposition for Automated Prototyping
February 28, 2025 Β· Declared Dead Β· π 2025 IEEE/ACM 47th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
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Authors
Kristian Kolthoff, Felix Kretzer, Christian Bartelt, Alexander Maedche, Simone Paolo Ponzetto
arXiv ID
2502.21068
Category
cs.SE: Software Engineering
Citations
4
Venue
2025 IEEE/ACM 47th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
Last Checked
4 months ago
Abstract
GUI prototyping serves as one of the most valuable techniques for enhancing the elicitation of requirements and facilitating the visualization and refinement of customer needs. While GUI prototyping has a positive impact on the software development process, it simultaneously demands significant effort and resources. The emergence of Large Language Models (LLMs) with their impressive code generation capabilities offers a promising approach for automating GUI prototyping. Despite their potential, there is a gap between current LLM-based prototyping solutions and traditional user-based GUI prototyping approaches which provide visual representations of the GUI prototypes and direct editing functionality. In contrast, LLMs and related generative approaches merely produce text sequences or non-editable image output, which lacks both mentioned aspects and therefore impede supporting GUI prototyping. Moreover, minor changes requested by the user typically lead to an inefficient regeneration of the entire GUI prototype when using LLMs directly. In this work, we propose GUIDE, a novel LLM-driven GUI generation decomposition approach seamlessly integrated into the popular prototyping framework Figma. Our approach initially decomposes high-level GUI descriptions into fine-granular GUI requirements, which are subsequently translated into Material Design GUI prototypes, enabling higher controllability and more efficient adaption of changes. To efficiently conduct prompting-based generation of Material Design GUI prototypes, we propose a retrieval-augmented generation approach to integrate the component library. Our preliminary evaluation demonstrates the effectiveness of GUIDE in bridging the gap between LLM generation capabilities and traditional GUI prototyping workflows, offering a more effective and controlled user-based approach to LLM-driven GUI prototyping. Video: https://youtu.be/C9RbhMxqpTU
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