World Model for Testing Autonomous Systems Using Petri Nets

This paper describes a model-based test generation approach for testing autonomous systems interacting with their environment (i.e., world). Unlike other approaches that assume a static world with attributes and values, we present and test a dynamic world. We use Petri Nets to present a world model that describes behaviors of world entities (i.e., actors). Abstract World Behavioral Test Cases (AWBTCs) are then generated by covering the world model using graph coverage criteria. We also generate test-data by input-space partitioning to transform the generated AWBTCs into executable test cases. We apply the World Model-based Test Generation (WMBTG) technique to a case study in the Human-Robot Interaction (HRI) domain. Efficiency of coverage criteria is discussed.