TaOPT : Tool-Agnostic Optimization of Parallelized Automated Mobile UI Testing
Dezhi Ran, Zihe Song, Wenyu Wang, Wei Ming Yang, Tao Xie · 2025
The emergence of modern testing clouds, equipped with a vast array of real testing devices and high-fidelity emulators, has significantly increased the need for parallel automated mobile testing to optimally utilize the resources of testing clouds. Parallel testing aligns perfectly with the characteristic of rapid iteration cycles for mobile app development, where testing time is limited. While numerous tools have been proposed for optimizing the testing effectiveness on a single testing device, it remains an open problem to optimize the parallelization of automated mobile UI testing in terms of resource and time utilization. To optimize the parallelization of automated mobile UI testing, in this paper, we propose TaOPT, a fully automated, tool-agnostic approach, which improves the parallelization effectiveness of any given testing tool without modifying the tool's internal workflow. In particular, TaOPT conducts online analysis to infer loosely coupled UI subspaces in the App Under Test (AUT). TaOPT then manages access to these subspaces across various testing devices, guiding automated UI testing toward distinct subspaces on different devices without knowing the testing tool's internal workflow. We apply TaOPT on 18 highly popular mobile apps with three state-of-the-art automated UI testing tools for Android. Evaluation results show that TaOPT helps the tools reach comparable code coverage using 60% less testing duration and 62% less machine time than the baseline on average. In addition, TaOPT consistently enhances automated UI testing tools to detect 1.2 to 2.1 times more unique crashes given the same testing resources.