Automatic HMI Structure Exploration Via Curiosity-Based Reinforcement Learning

Discovering the underlying structure of HMI software efficiently and sufficiently for the purpose of testing without any prior knowledge on the software logic remains a difficult problem. The key challenge lies in the complexity of the HMI software and the high variance in the coverage of current methods. In this paper, we introduce the PathFinder, an effective and automatic HMI software exploration framework. PathFinder adopts a curiosity-based reinforcement learning framework to choose actions that lead to the discovery of more unknown states. Additionally, PathFinder progressively builds a navigation model during the exploration to further improve state coverage. We have conducted experiments on both simulations and real-world HMI software testing environment, which comprise a full tool chain of automobile dashboard instrument cluster. The exploration coverage outperforms manual and fuzzing methods which are the current industrial standards.