论文标题
漂移：功能软件测试的深入增强学习
DRIFT: Deep Reinforcement Learning for Functional Software Testing
论文作者
论文摘要
有效的软件测试对于生产软件开发和可靠的用户体验至关重要。由于人体测试效率低下且昂贵，因此需要自动化的软件测试。在这项工作中，我们提出了一个名为Drift的功能软件测试的加固学习（RL）框架。漂移以用户界面的符号表示。它通过批处理-RL使用Q学习，并使用图神经网络对状态行动值函数进行建模。我们将漂移应用于测试Windows 10操作系统，并表明漂移可以以完全自动化的方式牢固地触发所需的软件功能。我们的实验测试了在不同应用程序上执行单个任务和组合任务的能力，这表明我们的框架可以通过各种测试目标有效测试软件。
Efficient software testing is essential for productive software development and reliable user experiences. As human testing is inefficient and expensive, automated software testing is needed. In this work, we propose a Reinforcement Learning (RL) framework for functional software testing named DRIFT. DRIFT operates on the symbolic representation of the user interface. It uses Q-learning through Batch-RL and models the state-action value function with a Graph Neural Network. We apply DRIFT to testing the Windows 10 operating system and show that DRIFT can robustly trigger the desired software functionality in a fully automated manner. Our experiments test the ability to perform single and combined tasks across different applications, demonstrating that our framework can efficiently test software with a large range of testing objectives.
