Humanoid: A Deep Learning-Based Approach to Automated Black-box Android App Testing

Automated input generators must constantly choose which UI element to interact with and how to interact with it, in order to achieve high coverage with a limited time budget. Currently, most black-box input generators adopt pseudo-random or brute-force searching strategies, which may take very long to find the correct combination of inputs that can drive the app into new and important states. We propose Humanoid, an automated black-box Android app testing tool based on deep learning. The key technique behind Humanoid is a deep neural network model that can learn how human users choose actions based on an app's GUI from human interaction traces. The learned model can then be used to guide test input generation to achieve higher coverage. Experiments on both open-source apps and market apps demonstrate that Humanoid is able to reach higher coverage, and faster as well, than the state-of-the-art test input generators. Humanoid is open-sourced at https://github.com/yzygitzh/Humanoid and a demo video can be found at https://youtu.be/PDRxDrkyORs.