Many-Objective Neuroevolution for Testing Games

TL;DR

This paper enhances game testing by transforming a neuroevolution-based test generator into a many-objective algorithm, significantly improving coverage and reducing search time in automated game testing.

Contribution

It introduces a many-objective neuroevolution approach for game testing, combining NEATEST with established algorithms to improve efficiency and effectiveness.

Findings

Average branch coverage increased from 75.88% to 81.33%.

Search time was reduced by 93.28%.

Multi-objective approach outperforms single-objective in game testing.

Abstract

Generating tests for games is challenging due to the high degree of randomisation inherent to games and hard-to-reach program states that require sophisticated gameplay. The test generator NEATEST tackles these challenges by combining search-based software testing principles with neuroevolution to optimise neural networks that serve as test cases. However, since NEATEST is designed as a single-objective algorithm, it may require a long time to cover fairly simple program states or may even get stuck trying to reach unreachable program states. In order to resolve these shortcomings of NEATEST, this work aims to transform the algorithm into a many-objective search algorithm that targets several program states simultaneously. To this end, we combine the neuroevolution algorithm NEATEST with the two established search-based software testing algorithms, MIO and MOSA. Moreover, we adapt the existing many-objective neuroevolution algorithm NEWS/D to serve as a test generator. Our experiments on a dataset of 20 SCRATCH programs show that extending NEATEST to target several objectives simultaneously increases the average branch coverage from 75.88% to 81.33% while reducing the required search time by 93.28%.