Semantic analysis for deep Q-network in android GUI testing

Since the big boom of smartphone and consequently of mobile applications, developers nowadays have many tools to help them create applications easier and faster. However, efficient automated testing tools are still missing, especially for GUI testing. We propose an automated GUI testing tool for Android applications using Deep Q-Network and semantic analysis of the GUI. We identify the semantic meanings of GUI elements and use them as an input to a neural network, which through training, approximates the behavioral model of the application under test. The neural network is trained using the Q-Learning algorithm of Reinforcement Learning. It guides the testing tool to explore more often functionalities that can only be accessed through a specific sequence of actions. The tool does not require access to the source code of the application under test. It obtains higher code coverage and is better at fault detection in comparison to state-of-the-art testing tools.