  This paper investigates the application of large language models (LLM) in the
domain of mobile application test script generation. Test script generation is
a vital component of software testing, enabling efficient and reliable
automation of repetitive test tasks. However, existing generation approaches
often encounter limitations, such as difficulties in accurately capturing and
reproducing test scripts across diverse devices, platforms, and applications.
These challenges arise due to differences in screen sizes, input modalities,
platform behaviors, API inconsistencies, and application architectures.
Overcoming these limitations is crucial for achieving robust and comprehensive
test automation.
  By leveraging the capabilities of LLMs, we aim to address these challenges
and explore its potential as a versatile tool for test automation. We
investigate how well LLMs can adapt to diverse devices and systems while
accurately capturing and generating test scripts. Additionally, we evaluate its
cross-platform generation capabilities by assessing its ability to handle
operating system variations and platform-specific behaviors. Furthermore, we
explore the application of LLMs in cross-app migration, where it generates test
scripts across different applications and software environments based on
existing scripts.
  Throughout the investigation, we analyze its adaptability to various user
interfaces, app architectures, and interaction patterns, ensuring accurate
script generation and compatibility. The findings of this research contribute
to the understanding of LLMs' capabilities in test automation. Ultimately, this
research aims to enhance software testing practices, empowering app developers
to achieve higher levels of software quality and development efficiency.
