Deeply Reinforcing Android GUI Testing with Deep Reinforcement Learning

Abstract

As the scale and complexity of Android applications continue to grow in response to increasing market and user demands, quality assurance challenges become more significant. While previous studies have demonstrated the superiority of Reinforcement Learning (RL) in Android GUI testing, its effectiveness remains limited, particularly in large, complex apps. This limitation arises from the ineffectiveness of Tabular RL in learning the knowledge within the large state-action space of the App Under Test (AUT) and from the suboptimal utilization of the acquired knowledge when employing more advanced RL techniques. To address such limitations, this paper presents DQT, a novel automated Android GUI testing approach based on deep reinforcement learning. DQT preserves widgets’ structural and semantic information with graph embedding techniques, building a robust foundation for identifying similar states or actions and distinguishing different ones. Moreover, a specially designed Deep Q-Network (DQN) effectively guides curiosity-driven exploration by learning testing knowledge from runtime interactions with the AUT and sharing it across states or actions. Experiments conducted on 30 diverse open-source apps demonstrate that DQT outperforms existing state-of-the-art testing approaches in both code coverage and fault detection, particularly for large, complex apps. The faults detected by DQT have been reproduced and reported to developers; so far, 21 of the reported issues have been explicitly confirmed, and 14 have been fixed.