一句话：提出工具无关的 TaOPT，通过在线分析推断松耦合 UI 子空间并跨设备调度，显著提升并行移动 UI 测试效率。
现代测试云配备大量真机与高保真模拟器，如何并行化自动化移动 UI 测试以充分利用资源与时间仍是开放问题：已有工作多聚焦单设备上的测试有效性优化。TaOPT 是完全自动、工具无关的方法，不修改给定测试工具的内部工作流：它在线分析被测应用，推断松耦合的 UI 子空间，并在多台设备间管理对这些子空间的访问，引导不同设备上的测试探索不同子空间。评估在 18 个热门移动应用与三款最先进的 Android 自动化 UI 测试工具上进行：TaOPT 平均以少 60% 的测试时长、少 62% 的机器时间达到与基线相当的代码覆盖率，并在同等资源下将唯一崩溃检出数提升 1.2–2.1 倍。
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.
@inproceedings{RanSW0X25,
title = {<scp>TaOPT</scp>
: Tool-Agnostic Optimization of Parallelized Automated Mobile UI Testing},
author = {Dezhi Ran and Zihe Song and Wenyu Wang and Wei Yang and Tao Xie},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3676641.3716282}
}