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.

翻译：配置是定制复杂软件系统、服务和产品的关键技术。配置器的成功应用不仅取决于技术正确性、性能和领域建模，还取决于其可用性。虽然通用可用性启发式方法被广泛使用，但针对配置器的特定标准和系统化用户界面（UI）分析工具支持仍然有限。本文探索了利用多模态大语言模型（MLLMs）对配置器用户界面进行可扩展的半自动化可用性分析。我们从文献中综合了18项配置器特定的可用性标准，并将这些标准应用于基于MLLM的16个真实配置器的分析中。每个标准被单独评估，以生成可用性问题的严重性评级和可操作的改进建议。对结果的审查证实，MLLM能够可靠地识别配置器特定的可用性问题，并提供具有领域意识的改进建议。尽管人工验证仍然是必要的，但该方法有潜力显著减少分析配置器可用性所需的工作量。