Summary of the Dissertation

Self-learning systems that individually adapt their behavior to users during operation are becoming increasingly important—for example, in vehicles that remember personal driving habits.

However, validating such dynamically learning functions poses significant challenges for traditional testing methods.

This dissertation therefore presents a novel validation approach that combines realistic usage scenarios with intelligent testing methods to verify the behavior of self-adaptive systems in a targeted and traceable manner.

We warmly congratulate Marco Stang on this significant achievement and wish him all the best for his academic and professional future.