GPT‐Monkey: Enhancing Automated GUI Testing for Android Apps via LLM‐Driven Interface Understanding and Function Segmentation

Automated graphical user interface (GUI) testing is essential for ensuring mobile app quality. However, existing related methods lack deep GUI understanding and cannot segment specific functions for targeted testing, resulting in wasted test events and low efficiency. This study aims to leverage large language models (LLMs) to enhance Monkey‐based GUI testing by enabling function‐level segmentation and tailored parameter generation driven by user requirements. We propose GPT‐Monkey, which integrates Monkey’s randomness with LLM’s understanding capability. It establishes global interface associations and cross‐modal alignment for LLM‐driven function segmentation, employs parameter‐retrieval augmented generation (RAG) to guide tailored parameter generation, and adopts a dual‐feedback mechanism to iteratively optimize testing. Experiments show that GPT‐Monkey improves crash detection by 16.7% and efficiency by 34.5% over the optimal baseline, and achieves 95% function segmentation accuracy and uncovers 397 real‐world crashes on 1000 Google Play apps. The results demonstrate that LLM‐driven function segmentation and tailored parameter generation significantly enhance the precision and efficiency of automated GUI testing.