  Robotic code needs to be verified to ensure its safety and functional
correctness, especially when the robot is interacting with people. Testing real
code in simulation is a viable option. However, generating tests that cover
rare scenarios, as well as exercising most of the code, is a challenge
amplified by the complexity of the interactions between the environment and the
software. Model-based test generation methods can automate otherwise manual
processes and facilitate reaching rare scenarios during testing. In this paper,
we compare using Belief-Desire-Intention (BDI) agents as models for test
generation with more conventional automata-based techniques that exploit model
checking, in terms of practicality, performance, transferability to different
scenarios, and exploration (`coverage'), through two case studies: a
cooperative manufacturing task, and a home care scenario. The results highlight
the advantages of using BDI agents for test generation. BDI agents naturally
emulate the agency present in Human-Robot Interactions (HRIs), and are thus
more expressive than automata. The performance of the BDI-based test generation
is at least as high, and the achieved coverage is higher or equivalent,
compared to test generation based on model checking automata.
