A system built in terms of autonomous software agents may require even greater correctness assurance than one that is merely reacting to the immediate control of its users.
Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents
may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents
requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents’ developers. In this paper, we address this problem, introducing and evaluating an approach
to testing autonomous agents that uses evolutionary optimisation to generate demanding test
cases. We propose a methodology to derive objective (fitness) functions that drive evolutionary algorithms, and evaluate the overall approach with two simulated autonomous agents. The
obtained results show that our approach is effective in finding good test cases automatically
