Combining Neuroevolution with the Search for Novelty to Improve the Generation of Test Inputs for Games
This work addresses a specific issue in automated software testing for games, but it is incremental as it builds on existing neuroevolution techniques.
The paper tackled the problem of challenging fitness landscapes in the Neatest approach for generating test inputs for games by promoting novel behaviors during the search, resulting in a promising method demonstrated on two Scratch games.
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.