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This paper investigates the application of multimodal large language models (MLLMs) for semi-automated usability analysis of configurator user interfaces. They synthesize 18 configurator-specific usability criteria from the literature and use MLLMs to assess 16 real-world configurators against these criteria, generating severity ratings and improvement suggestions. Results show that MLLMs can reliably identify usability issues and provide domain-aware recommendations, reducing the effort required for usability analysis.
MLLMs can now reliably critique your configurator UI, flagging usability issues and suggesting improvements with surprising domain awareness.
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.
