  Test generation at the graphical user interface (GUI) level has proven to be
an effective method to reveal faults. When doing so, a test generator has to
repeatably decide what action to execute given the current state of the system
under test (SUT). This problem of action selection usually involves random
choice, which is often referred to as monkey testing. Some approaches leverage
other techniques to improve the overall effectiveness, but only a few try to
create human-like actions---or even entire action sequences. We have built a
novel session-based recommender system that can guide test generation. This
allows us to mimic past user behavior, reaching states that require complex
interactions. We present preliminary results from an empirical study, where we
use GitHub as the SUT. These results show that recommender systems appear to be
well-suited for action selection, and that the approach can significantly
contribute to the improvement of GUI-based test generation.
