Usability Analysis of Configurator User Interfaces with Multimodal Large Language Models
For configurator developers and usability experts, this work provides a scalable tool for systematic UI analysis, though it is incremental as it applies existing MLLMs to a new domain.
The paper explores using multimodal large language models (MLLMs) to semi-automate usability analysis of configurator user interfaces, synthesizing 18 configurator-specific criteria and testing on 16 real-world configurators. Results show MLLMs can reliably identify usability issues and provide actionable suggestions, potentially reducing analysis effort.
Configuration is a key technology for tailoring complex software systems, services, and products. A successful application of configurators not only depends on technical correctness, performance, and domain modeling but also on their usability. While general usability heuristics are widely used, configurator-specific criteria and tool support for systematic user interface (UI) analysis are limited. This paper explores the use of multimodal large language models (MLLMs) for scalable and semi-automated usability analysis of configurator UIs. We synthesize 18 configurator-specific usability criteria from the literature and apply these criteria in an MLLM-based analysis of 16 real-world configurators. Each criterion is assessed individually to generate severity ratings for usability issues and actionable improvement suggestions. A review of the results confirms that MLLMs can reliably identify configurator-specific usability issues and provide domain-aware improvement recommendations. Although human validation remains necessary, this approach has the potential to significantly reduce the required effort to analyze configurator usability.