Prioritizing automated user interface tests using reinforcement learning

User interface testing validates the correctness of an application through visual cues and interactive events emitted in real world usages. Performing user interface tests is a time-consuming process, and thus, many studies have focused on prioritizing test cases to help maintain the effectiveness of testing while reducing the need for a full execution. This paper describes a novel prioritization method that combines Reinforcement Learning and interaction coverage testing concepts. While Reinforcement Learning has been found to be suitable for rapid changing projects with abundant historical data, interaction coverage considers in depth the event-based aspects of user interface testing and provides a granular level at which the Reinforcement Learning system can gain more insights into individual test cases. We experiment and assess the proposed method using five data sets, finding that the method outperforms related methods and has the potential to be used in practice.