A Deep Reinforcement Learning-Based Approach for Android GUI Testing
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
The mobile application market is booming, and Android applications occupy a vast market share. However, the applications may contain many errors. The task in the testing phase is to find these errors as soon as possible. It is urgent to test the application rapidly and effectively. Otherwise, it may affect user experience and cause substantial economic losses. Mobile applications iterate continuously to consummate performance and functional requirements, which leads to the increased complexity of applications and explosive growth of state combinations. Reinforcement learning aims to learn strategies to achieve specific goals by maximizing rewards. This paper applies it to Android GUI testing. In this paper, we propose ATAC. It is black-box based and adopts Advantage Actor-Critic (A2C) algorithm, which contains an actor (policy) and a critic (value function) to generate test cases automatically through deep reinforcement learning. To verify the validity of the proposed approach, we conducted our experiment on seventeen open-source applications from Github. Compared with ARES and Monkey, ATAC shows higher code coverage and detects more errors.
Advantage actor critic, Android GUI testing, Reinforement learning
Yuemeng Gao, Chuanqi Tao, Hongjing Guo, and Jerry Gao. "A Deep Reinforcement Learning-Based Approach for Android GUI Testing" Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2023): 262-276. https://doi.org/10.1007/978-3-031-25201-3_20