Towards Simulating User Behavior for Automating Usability Tests by Employing Large Language Models

Abstract

Large Language Models (LLMs) enable the automation of tasks that typically require substantial manual effort. This work investigates their applicability in the context of usability testing. First, we evaluate whether LLM-agents can navigate in and interact with different applications to accomplish given tasks. Second, we compare LLM-generated streams-of-thought with human think-aloud comments collected during usability tests. Results show that, based on GPT-4o, LLM-agents can successfully interact with websites and perform tasks such as information search. However, they often fail to recognize task completion and tend to engage in actions beyond the intended goals. The comparison further reveals clear differences between LLM-based and human observations: while human users overlook certain issues, LLM-agents identify them. These findings demonstrate the potential of LLMs as a preparatory step in usability testing and outline directions for advancing their adaptation and improvement.