Interlinking User Stories and GUI Prototyping: A Semi-Automatic LLM-based Approach

• URL: http://arxiv.org/abs/2406.08120v1
• Date: Wed, 12 Jun 2024 11:59:26 GMT
• Title: Interlinking User Stories and GUI Prototyping: A Semi-Automatic LLM-based Approach
• Authors: Kristian Kolthoff, Felix Kretzer, Christian Bartelt, Alexander Maedche, Simone Paolo Ponzetto,
• Abstract summary: We present a novel Large Language Model (LLM)-based approach for validating the implementation of functional NL-based requirements in a graphical user interface (GUI) prototype. Our approach aims to detect functional user stories that are not implemented in a GUI prototype and provides recommendations for suitable GUI components directly implementing the requirements.
• Score: 55.762798168494726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interactive systems are omnipresent today and the need to create graphical user interfaces (GUIs) is just as ubiquitous. For the elicitation and validation of requirements, GUI prototyping is a well-known and effective technique, typically employed after gathering initial user requirements represented in natural language (NL) (e.g., in the form of user stories). Unfortunately, GUI prototyping often requires extensive resources, resulting in a costly and time-consuming process. Despite various easy-to-use prototyping tools in practice, there is often a lack of adequate resources for developing GUI prototypes based on given user requirements. In this work, we present a novel Large Language Model (LLM)-based approach providing assistance for validating the implementation of functional NL-based requirements in a GUI prototype embedded in a prototyping tool. In particular, our approach aims to detect functional user stories that are not implemented in a GUI prototype and provides recommendations for suitable GUI components directly implementing the requirements. We collected requirements for existing GUIs in the form of user stories and evaluated our proposed validation and recommendation approach with this dataset. The obtained results are promising for user story validation and we demonstrate feasibility for the GUI component recommendations.

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