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

Abstract

Automated input generators are widely used for large-scale dynamic analysis of mobile apps. Such 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 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. In this paper, we propose Humanoid, a deep learning-based approach to GUI test input generation by learning from human interactions. Our insight is that if we can learn from human-generated interaction traces, it is possible to automatically prioritize test inputs based on their importance as perceived by users. We design and implement a deep neural network model to learn how end-users would interact with an app (specifically, which UI elements to interact with and how). Our experiments showed that the interaction model can successfully prioritize user-preferred inputs for any new UI (with a top-1 accuracy of 51.2% and a top-10 accuracy of 85.2%). We implemented an input generator for Android apps based on the learned model and evaluated it on both open-source apps and market apps. The results indicated that Humanoid was able to achieve higher coverage than six state-of-the-art test generators. However, further analysis showed that the learned model was not the main reason of coverage improvement. Although the learned interaction pattern could drive the app into some important GUI states with higher probabilities, it had limited effect on the width and depth of GUI state search, which is the key to improve test coverage in the long term. Whether and how human interaction patterns can be used to improve coverage is still an unknown and challenging problem.