As games challenge traditional automated white-box test generators, the Neatest approach generates test suites consisting of neural networks that exercise the source code by playing the games. Neatest generates these neural networks using an evolutionary algorithm that is guided by an objective function targeting individual source code statements. This approach works well if the objective function provides sufficient guidance, but deceiving or complex fitness landscapes may inhibit the search. In this paper, we investigate whether the issue of challenging fitness landscapes can be addressed by promoting novel behaviours during the search. Our case study on two Scratch games demonstrates that rewarding novel behaviours is a promising approach for overcoming challenging fitness landscapes, thus enabling future research on how to adapt the search algorithms to best use this information.

翻译：随着游戏对传统自动化白盒测试生成器提出挑战，Neatest方法通过生成由神经网络组成的测试套件，这些神经网络通过玩游戏来执行源代码。Neatest使用进化算法生成这些神经网络，该算法由针对单个源代码语句的目标函数引导。如果目标函数提供足够的指导，这种方法效果良好，但具有欺骗性或复杂的适应度地形可能会抑制搜索。在本文中，我们研究是否可以通过在搜索过程中促进新颖行为来解决具有挑战性的适应度地形问题。我们在两个Scratch游戏上的案例研究表明，奖励新颖行为是克服具有挑战性的适应度地形的一种有前景的方法，从而为未来研究如何调整搜索算法以最佳利用此信息奠定了基础。