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Although automated test generation is common in many programming domains,games still challenge test generators due to their heavy randomisation andhard-to-reach program states. Neuroevolution combined with search-basedsoftware testing principles has been shown to be a promising approach fortesting games, but the co-evolutionary search for optimal network topologiesand weights involves unreasonably long search durations. In this paper, we aimto improve the evolutionary search for game input generators by integratingknowledge about human gameplay behaviour. To this end, we propose a novel wayof systematically recording human gameplay traces, and integrating these tracesinto the evolutionary search for networks using traditional gradient descent asa mutation operator. Experiments conducted on eight diverse Scratch gamesdemonstrate that the proposed approach reduces the required search time fromfive hours down to only 52 minutes.

Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution