  Thorough testing of safety-critical autonomous systems, such as self-driving
cars, autonomous robots, and drones, is essential for detecting potential
failures before deployment. One crucial testing stage is model-in-the-loop
testing, where the system model is evaluated by executing various scenarios in
a simulator. However, the search space of possible parameters defining these
test scenarios is vast, and simulating all combinations is computationally
infeasible. To address this challenge, we introduce AmbieGen, a search-based
test case generation framework for autonomous systems. AmbieGen uses
evolutionary search to identify the most critical scenarios for a given system,
and has a modular architecture that allows for the addition of new systems
under test, algorithms, and search operators. Currently, AmbieGen supports test
case generation for autonomous robots and autonomous car lane keeping assist
systems. In this paper, we provide a high-level overview of the framework's
architecture and demonstrate its practical use cases.
