Human-AI Collaborative Game Testing with Vision Language Models

TL;DR

This paper explores how AI, specifically vision language models, can assist human testers in video game testing, improving defect detection efficiency and accuracy, while addressing challenges posed by AI errors.

Contribution

It develops and evaluates an AI-assisted workflow for game testing, demonstrating AI's potential and limitations in enhancing human performance.

Findings

AI assistance improves defect detection performance.

Detailed knowledge enhances AI effectiveness.

AI errors can negatively impact human decision-making.

Abstract

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.