Learning to Identify Perceptual Bugs in 3D Video Games
This addresses the challenge of detecting bugs that cannot be identified with manual rules for game developers and testers, representing a novel approach in automated bug detection.
The paper tackled the problem of identifying perceptual bugs in 3D video games by using learning-based methods that rely solely on the rendered game screen, resulting in the development of World of Bugs (WOB), an open platform for testing automated bug detection methods.
Automated Bug Detection (ABD) in video games is composed of two distinct but complementary problems: automated game exploration and bug identification. Automated game exploration has received much recent attention, spurred on by developments in fields such as reinforcement learning. The complementary problem of identifying the bugs present in a player's experience has for the most part relied on the manual specification of rules. Although it is widely recognised that many bugs of interest cannot be identified with such methods, little progress has been made in this direction. In this work we show that it is possible to identify a range of perceptual bugs using learning-based methods by making use of only the rendered game screen as seen by the player. To support our work, we have developed World of Bugs (WOB) an open platform for testing ABD methods in 3D game environments.