Integrating LLM-Based Text Generation with Dynamic Context Retrieval for GUI Testing
Juyeon Yoon, Seah Kim, Somin Kim, Se Young Jung, Shin Yoo · 2025
Automated GUI testing plays a crucial role for smartphone vendors who have to ensure that the widely used mobile apps-that are not essentially developed by the vendors-are compatible with new devices and system updates. While existing testing techniques can automatically generate event sequences to reach different GUI views, inputs such as strings and numbers remain difficult to generate, as their generation often involves semantic understanding of the app functionality. Recently, Large Language Models (LLMs) have been successfully adopted to generate string inputs that are semantically relevant to the test case. This paper evaluates the LLM-based input generation in the industrial context of vendor testing of both in-house and 3rd party mobile apps. We present DROIDFILLER, an LLM based input generation technique that builds upon existing work with more sophisticated prompt engineering and customisable context retrieval. DROIDFILLER is empirically evaluated using a total of 120 textfields collected from a total of 45 apps, including both in-house and 3rd party ones. The results show that DROIDFILLER can outperform both vanilla LLM based input generation as well as the existing resource pool approach. We integrate DROIDFILLER into the existing GUI testing framework used at Samsung, evaluate its performance, and discuss the challenges and considerations for practical adoption of LLM-based input generation in the industry.