Software Engineering and Testing for AI Systems

How do you know and assess whether an AI-intensive system is working as intended? On the one hand, quality assurance for such systems can be considered to be a software testing challenge; in parallel, the machine learning components within such systems are normally validated according to machine learning-specific evaluation methodology.

In this course, you will learn more about these two complementary takes to evaluation and quality assurance, while learning about (and getting hands-on experience in) state-of-the-art advances in combining these two takes.

Course content taught includes:

• Basics of testing (assertions, white-box vs. black-box testing)

• Basics of machine learning evaluation (validation en validity)

• Quality assurance at different scopes in AI-intensive systems (models, components, systems, cyber-physical systems)

• Adversarial attacks

• Software testing techniques (e.g. fuzzing, metamorphic testing, combinatorial testing, differential testing)

• Data labels (ground truth/oracle) validation