Test case generation for agent-based models: A systematic literature review
This review identifies gaps in testing methods for agent-based models, which are crucial for preventing software faults that could lead to poor decisions in complex simulations, but it is incremental as it synthesizes existing research.
The paper conducted a systematic literature review on test case generation for agent-based models, addressing five research questions about information artifacts, generation methods, verdict assignment, adequacy measurement, and abstraction levels, finding that most techniques are effective for functional requirements at agent and integration levels but lack for society-level behavior.
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.