Users interact with modern applications and devices through graphical user interfaces (GUIs). To ensure intuitive and easy usability, the GUIs need to be tested, where developers try to find possible bugs and inconsistent functionality. Manual GUI testing requires time and effort, and thus, its efficiency can be improved with automation. Conventional automation tools, for GUI testing, reduce the burden of manual testing but also introduce challenges in the maintenance of test cases. In order to overcome these issues, we propose a deep-reinforcement-learning-based (DRL) solution for automated and adaptive GUI testing. Specifically, we propose and evaluate the performance of an image-based DRL solution. We adapt the asynchronous advantage actor-critic (A3C) algorithm to GUI testing inspired by how a human uses a GUI. We feed screenshots of the GUI as the input and let the algorithm decide how to interact with GUI components. Finally, we compare the performance of our solution against selected baseline methods and human users. We observe that our solution can achieve up to six times higher exploration efficiency compared to the other baseline algorithms. Moreover, our solution manages to perform almost as well as an experienced user in our experimental GUI testing scenario. For these reasons, image-based DRL exploration can be considered as a viable GUI testing method.

Juha Eskonen is an Experienced Developer at Ericsson. He completed a Masters with research thesis from Aalto University in 2019 on the topic of deep reinforcement learning.