  The challenges of robotic software testing extend beyond conventional
software testing. Valid, realistic and interesting tests need to be generated
for multiple programs and hardware running concurrently, deployed into dynamic
environments with people. We investigate the use of Belief-Desire-Intention
(BDI) agents as models for test generation, in the domain of human-robot
interaction (HRI) in simulations. These models provide rational agency,
causality, and a reasoning mechanism for planning, which emulate both
intelligent and adaptive robots, as well as smart testing environments directed
by humans. We introduce reinforcement learning (RL) to automate the exploration
of the BDI models using a reward function based on coverage feedback. Our
approach is evaluated using a collaborative manufacture example, where the
robotic software under test is stimulated indirectly via a simulated human
co-worker. We conclude that BDI agents provide intuitive models for test
generation in the HRI domain. Our results demonstrate that RL can fully
automate BDI model exploration, leading to very effective coverage-directed
test generation.
