一句话：提出基于图像的 AdaT 方法，动态推断 GUI 渲染状态以调整事件间隔，兼顾测试效率与有效性。
Android 应用质量保障催生了大量自动化 GUI 测试工具，测试算法虽不断改进，GUI 渲染的影响却被忽视：等待时间过长会拖慢测试，过短则使事件在未渲染完成的界面上执行，降低测试有效性。本文提出 AdaT，一种轻量级、基于图像的动态调整事件间隔方法。AdaT 接收 GUI 实时流，用深度学习模型推断当前渲染状态，并与测试工具同步，在 GUI 完全渲染后再调度下一个事件，从而在有效性与效率之间取得平衡。评估表明该方法在准确性、效率和有效性上均有良好表现；作者还将其与现有自动化测试工具集成，证明 AdaT 能覆盖更多 Activity 并在完全渲染的 GUI 上执行更多事件。
Due to the importance of Android app quality assurance, many automated GUI testing tools have been developed. Although the test algorithms have been improved, the impact of GUI rendering has been overlooked. On the one hand, setting a long waiting time to execute events on fully rendered GUIs slows down the testing process. On the other hand, setting a short waiting time will cause the events to execute on partially rendered GUIs, which negatively affects the testing effectiveness. An optimal waiting time should strike a balance between effectiveness and efficiency. We propose AdaT, a lightweight image-based approach to dynamically adjust the inter-event time based on GUI rendering state. Given the real-time streaming on the GUI, AdaT presents a deep learning model to infer the rendering state, and synchronizes with the testing tool to schedule the next event when the GUI is fully rendered. The evaluations demonstrate the accuracy, efficiency, and effectiveness of our approach. We also integrate our approach with the existing automated testing tool to demonstrate the usefulness of AdaT in covering more activities and executing more events on fully rendered GUIs.