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

arXiv: https://arxiv.org/abs/2407.04985 · HTML: https://mlsanity.com/paper/2407.04985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [A Survey of Video Game Testing](https://mlsanity.com/paper/2103.06431/) (2021)
- [Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance](https://mlsanity.com/paper/1902.03142/) (2019)
- [Automated Test Generation for Scratch Programs](https://mlsanity.com/paper/2202.06274/) (2022)
- [Neuroevolution-Based Generation of Tests and Oracles for Games](https://mlsanity.com/paper/2208.13632/) (2022)
- [Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution](https://mlsanity.com/paper/2304.06413/) (2023)

## Abstract (arXiv)

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.