Combining Neuroevolution with the Search for Novelty to Improve the Generation of Test Inputs for Games
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913419280515072 |
|---|---|
| author | Feldmeier, Patric Fraser, Gordon |
| author_facet | Feldmeier, Patric Fraser, Gordon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04985 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Combining Neuroevolution with the Search for Novelty to Improve the Generation of Test Inputs for Games Feldmeier, Patric Fraser, Gordon Software Engineering Machine Learning Neural and Evolutionary Computing 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. |
| title | Combining Neuroevolution with the Search for Novelty to Improve the Generation of Test Inputs for Games |
| topic | Software Engineering Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2407.04985 |