Human-AI Collaborative Game Testing with Vision Language Models
This addresses the costly and inefficient testing processes in the video game industry, offering practical insights for integration, but it is incremental as it builds on existing AI methods for a specific domain.
This study tackled the problem of inefficient manual game testing by developing an AI-assisted workflow using state-of-the-art models, finding that AI significantly improved defect identification in 800 test cases with 276 participants, especially when paired with detailed knowledge, though AI errors negatively impacted human decisions.
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.