论文标题
效率事项：通过GUI渲染推断加速自动测试
Efficiency Matters: Speeding Up Automated Testing with GUI Rendering Inference
论文作者
论文摘要
由于Android应用质量保证的重要性，已经开发了许多自动GUI测试工具。尽管测试算法得到了改善，但GUI渲染的影响被忽略了。一方面，设置漫长的等待时间来执行完全渲染的GUIS上的事件减慢了测试过程。另一方面，设置短期等待时间会导致事件在部分渲染的GUI上执行，从而对测试效率产生负面影响。最佳等待时间应在有效性和效率之间取得平衡。我们提出了ADAT，这是一种基于图像的轻巧方法，可动态调整基于GUI渲染状态的活动间时间。考虑到GUI上的实时流媒体，ADAT提出了一个深度学习模型，以推断渲染状态，并与测试工具同步，以安排GUI充分渲染时的下一个事件。评估证明了我们方法的准确性，效率和有效性。我们还将我们的方法与现有的自动测试工具集成在一起，以证明ADAT在涵盖更多活动和执行更多充分渲染GUI的活动中的实用性。
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.
