一句话：提出 WebCQ，用 QTRAN 协作多智能体深度强化学习与轻量同步机制，实现可扩展的大规模 Web GUI 测试。
现有基于多智能体强化学习（MARL）的 GUI 测试方法（如 MARG、Fastbot）受限于表格型强化学习算法，难以扩展到状态空间庞大、交互元素众多的现代 Web 应用。为此，本文提出 WebCQ：它引入 QTRAN 进行多智能体协调，并设计轻量同步机制以适配异步 Web 测试场景；同时为每个 UI 事件提取语义与探索特征，构成动作向量并与当前状态向量拼接后输入策略网络，从而在动态动作空间中实现基于 DQN 的决策。作者在八个大型商业网站上评估 WebCQ，结果表明：在相同时间预算与智能体数量下，WebCQ 比 MARG 多探索 33.3% 的状态、多执行 42.2% 的唯一动作，并在其中六个网站上触发更多失败；20 小时实验中其动作吞吐更高，且随智能体数量增加性能提升更明显，展现出良好的可扩展性。
Multi-agent reinforcement learning (MARL)-based techniques have shown promise for GUI testing. However, as the complexity of modern GUI software increases, existing MARL-based approaches (e.g., MARG and Fastbot) struggle to scale due to the inherent limitations of their underlying tabular reinforcement learning algorithms. This limits their applicability to large-scale commercial GUI software, especially web applications with vast state spaces and many interactive elements. To fill this gap, we propose WebCQ, a novel MARL-based approach for scalable web GUI testing. WebCQ incorporates QTRAN for multi-agent coordination and a lightweight synchronization mechanism, allowing it to work under asynchronous web testing scenarios. It extracts semantic and exploration features for each UI event to form an action vector. This vector is concatenated with the current state vector and fed into the policy network, enabling DQN-based decision making within a dynamic action space. We evaluated WebCQ on eight large-scale commercial websites. Under the same time budget and agent count, WebCQ explored 33.3% more states and executed 42.2% more unique actions than MARG, while triggering more failures on six of the eight websites under test. It also demonstrated strong scalability, maintaining higher action throughput during 20-hour experiments, and achieving greater performance improvements as the number of agents increased. These results show that WebCQ overcomes key limitations of existing MARL-based approaches, providing a scalable and effective solution to enhance modern web GUI testing.
@inproceedings{abs-2606-22502,
title = {WebCQ: Cooperative Multi-agent Deep Reinforcement Learning for Scalable Web GUI Testing},
author = {Yujia Fan and Sinan Wang and Zebang Fei and Yao Qin and Huaxuan Li and Yepang Liu},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3834374}
}