As modern video games become increasingly complex, traditional manual testing methods are proving costly and inefficient, limiting the ability to ensure high-quality game experiences. While advancements in Artificial Intelligence (AI) offer the potential to assist human testers, the effectiveness of AI in truly enhancing real-world human performance remains underexplored. This study investigates how AI can improve game testing by developing and experimenting with an AI-assisted workflow that leverages state-of-the-art machine learning models for defect detection. Through an experiment involving 800 test cases and 276 participants of varying backgrounds, we evaluate the effectiveness of AI assistance under four conditions: with or without AI support, and with or without detailed knowledge of defects and design documentation. The results indicate that AI assistance significantly improves defect identification performance, particularly when paired with detailed knowledge. However, challenges arise when AI errors occur, negatively impacting human decision-making. Our findings show the importance of optimizing human-AI collaboration and implementing strategies to mitigate the effects of AI inaccuracies. By this research, we demonstrate AI's potential and problems in enhancing efficiency and accuracy in game testing workflows and offers practical insights for integrating AI into the testing process.

翻译：随着现代视频游戏日益复杂，传统人工测试方法成本高、效率低，难以保障高质量游戏体验。人工智能（AI）的进步虽有望辅助人类测试人员，但AI在真正提升实际人类工作效能方面的有效性仍未得到充分探索。本研究通过开发并实验一种基于前沿机器学习模型的AI辅助缺陷检测工作流，探究AI如何改进游戏测试。我们设计了包含800个测试用例及276名不同背景参与者的实验，在有无AI支持与有无缺陷及设计文档详细知识四种条件下评估辅助效果。结果表明：AI辅助显著提升缺陷识别性能，尤其在结合详细知识时效果更佳；然而，AI出现的错误会负面影响人类决策。研究发现揭示了优化人机协作策略与规避AI误差影响的重要性。本研究证明了AI在提升游戏测试工作流效率与准确性方面的潜力与问题，并为将AI集成至测试流程提供了实践指导。