Combining Neuroevolution with the Search for Novelty to Improve the Generation of Test Inputs for Games

TL;DR

This paper explores combining neuroevolution with novelty search to enhance automated test input generation for games, addressing complex fitness landscapes by promoting behavioral diversity.

Contribution

It introduces a novel approach that integrates novelty search with neuroevolution to improve test input generation for games, especially in challenging fitness landscapes.

Findings

Rewarding novel behaviors helps overcome deceptive fitness landscapes.

Promoting behavioral diversity improves the effectiveness of test generation.

Case studies on Scratch games demonstrate the approach's potential.

Abstract

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.