This dissertation aims to investigate whether agents based on deep neural networks trained by evolutionary methods are suitable for automated testing of software applications at the GUI level. In our approach, the agents receive the visual view of the GUI surface in the form of pixel data and can use their outputs to control a virtual mouse or keyboard to navigate through the application under test (AUT). The agent’s goal during this process is to maximize test coverage to find previously undetected bugs.