Automated game testing using computer vision methods

Păduraru, Ciprian; Paduraru, Miruna; Ştefănescu, Alin

doi:10.1109/asew52652.2021.00024

Cited by 9 publications

(1 citation statement)

References 18 publications

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“…However, since games are commonly built incrementally, both approaches are not only time-consuming but also expensive and error-prone, as testers have to re-validate the same program in updated program versions repeatedly [29,32]. Various approaches for generating tests automatically have been proposed, such as combining reinforcement learning with computer vision techniques [28], employing evolutionary search to find simulation traces [6] or combining reinforcement learning with evolutionary algorithms and multi-objective optimisation [40]. To tackle the challenges of heavy program randomisation inherent to games, Neatest evolves test cases in the form of neural networks that produce input sequences dynamically based on the current state of the program [12].…”

Section: Related Workmentioning

confidence: 99%

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

Feldmeier,

Fraser

2024

Proceedings of the 1st ACM International Workshop on Foundations of Applied Software Engineering for Games

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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. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Search-based software engineering; • Computing methodologies → Search methodologies.

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