Towards Efficient Record and Replay: A Case Study in WeChat

TL;DR

This paper presents WeReplay, a deep learning-based, image-driven approach to optimize record-and-replay tools for WeChat by dynamically adjusting event timing based on GUI rendering states, improving efficiency and effectiveness.

Contribution

We introduce WeReplay, a novel, lightweight, deep learning-based method that accurately detects GUI rendering states to enhance replay performance in complex mobile apps.

Findings

Achieves 92.1% precision and 93.3% recall in GUI rendering state detection.

Successfully replays 23 common WeChat scenarios more efficiently than existing methods.

Improves replay accuracy and speed across different devices.

Abstract

WeChat, a widely-used messenger app boasting over 1 billion monthly active users, requires effective app quality assurance for its complex features. Record-and-replay tools are crucial in achieving this goal. Despite the extensive development of these tools, the impact of waiting time between replay events has been largely overlooked. On one hand, a long waiting time for executing replay events on fully-rendered GUIs slows down the process. On the other hand, a short waiting time can lead to events executing on partially-rendered GUIs, negatively affecting replay effectiveness. An optimal waiting time should strike a balance between effectiveness and efficiency. We introduce WeReplay, a lightweight image-based approach that dynamically adjusts inter-event time based on the GUI rendering state. Given the real-time streaming on the GUI, WeReplay employs a deep learning model to infer the rendering state and synchronize with the replaying tool, scheduling the next event when the GUI is fully rendered. Our evaluation shows that our model achieves 92.1% precision and 93.3% recall in discerning GUI rendering states in the WeChat app. Through assessing the performance in replaying 23 common WeChat usage scenarios, WeReplay successfully replays all scenarios on the same and different devices more efficiently than the state-of-the-practice baselines.