This paper proposes a method for utilizing large language model (LLM) inference results in the state space of deep reinforcement learning (DRL)-based GUI black-box testing for web applications. Previous research on mobile applications has shown that using HTML element interaction information in training improves the exploration performance of DRL agents. However, in web applications, obtaining interaction information can be difficult depending on the browser. Therefore, this study proposes a method that uses an LLM to infer clickability of elements, that is, one example of interaction information, from HTML and utilizes these results as part of the DRL state. Through experiments on a simple web application, we confirmed that using LLM-inferred values as state information improved learning efficiency and accuracy. Similar results were also demonstrated in experiments targeting a complex web application. The findings of this research enable browser-independent automation of GUI black-box testing and present a novel approach to utilizing LLMs for obtaining DRL states.
Sakai et al. (Thu,) studied this question.