Robotic Process Automation Efficiency for Mobile App Testing: An Empirical Investigation

Abstract

In today’s rapidly evolving software environment, the graphical user interface (GUI) plays a crucial role in providing intuitive, user-friendly interaction. However, traditional intrusive GUI testing methods often face challenges such as interrupting user workflows, requiring significant manual effort and insufficient test scenario coverage. Non-intrusive testing methods, such as Robotic Process Automation (RPA), offer a solution to validate GUI functionality without modifying the application’s code or affecting the user experience. RPA systems automate repetitive tasks by simulating user interactions, becoming valuable tools in GUI testing. However, challenges like limited computational resources, time constraints, or restricted exploration capabilities may limit the efficiency of individual RPA agents, thus restricting coverage and effectiveness. To address this issue, this study explores the performance of a single RPA agent versus an RPA cluster under different testing conditions, using three popular testing methods: Monkey, Stoat and Q-testing. Experimental results indicate that an RPA cluster outperforms a single RPA in GUI coverage and error detection, making a significant contribution to the field of non-intrusive GUI exploration testing. The findings of this study provide directions for future research to ensure the delivery of high-quality mobile applications.