  Although automated test generation is common in many programming domains,
games still challenge test generators due to their heavy randomisation and
hard-to-reach program states. Neuroevolution combined with search-based
software testing principles has been shown to be a promising approach for
testing games, but the co-evolutionary search for optimal network topologies
and weights involves unreasonably long search durations. In this paper, we aim
to improve the evolutionary search for game input generators by integrating
knowledge about human gameplay behaviour. To this end, we propose a novel way
of systematically recording human gameplay traces, and integrating these traces
into the evolutionary search for networks using traditional gradient descent as
a mutation operator. Experiments conducted on eight diverse Scratch games
demonstrate that the proposed approach reduces the required search time from
five hours down to only 52 minutes.
