Reveal-More: Amplifying Human Effort in Quality Assurance Testing Using Automated Exploration

Attempting to maximize coverage of a game via human gameplay is laborious and repetitive, introducing delays in the development process. Despite the importance of quality assurance (QA) testing, QA remains an underinvested area in the technical games research community. In this paper, we show that relatively simple automatic exploration techniques can be used to multiplicatively amplify coverage of a game starting from human tester data. Instead of attempting to displace human QA efforts, we seek to grow the impact that a human tester can make. Experiments with two games for the Super Nintendo Entertainment System highlight the qualitative and quantitative differences between isolated human and machine play compared to our hybrid approach called Reveal-More. We contribute a QA testing workflow that scales with the amount of human and machine time allocated to the effort.