  Agent-based models play an important role in simulating complex emergent
phenomena and supporting critical decisions. In this context, a software fault
may result in poorly informed decisions that lead to disastrous consequences.
The ability to rigorously test these models is therefore essential. In this
systematic literature review, we answer five research questions related to the
key aspects of test case generation in agent-based models: What are the
information artifacts used to generate tests? How are these tests generated?
How is a verdict assigned to a generated test? How is the adequacy of a
generated test suite measured? What level of abstraction of an agent-based
model is targeted by a generated test? Our results show that whilst the
majority of techniques are effective for testing functional requirements at the
agent and integration levels of abstraction, there are comparatively few
techniques capable of testing society-level behaviour. Additionally, we
identify a need for more thorough evaluation using realistic case studies that
feature challenging properties associated with a typical agent-based model.
