Test automation promises several advantages such as shorter lead times, higher code quality, and an executable documentation of the system's behavior. However, test automation won't deliver on those promises unless the quality of the automated test code itself is maintained, and to manually inspect the evolution of thousands of tests that change on a daily basis is impractical at best. This paper investigates how CodeScene -- a tool for predictive analyses and visualizations – could be used to identify technical debt in automated test code.