Self-driving cars are perceived as one of the most impactful applications of machine learning in society. At the core of these driverless vehicles are AI computing infrastructures built on deep neural networks that should be able to make thousands of reliable predictions in real time. This talk focuses on the peculiarities that concern the testing of deep neural networks for autonomous driving including topics such as offline and online testing, simulated vs real-world testing, and virtual and physical testing. We review the main research contributions in these areas and discuss some of the challenges and open problems to be addressed.

My interests concern devising techniques for testing web- and AI-based software systems. Over the years, I focused on solving problems related to different aspects of regression testing, such as test suite minimization, or improving the robustness and maintainability of test suites for web applications.