Automated Bug Detection in Modern Gaming Ecosystems

The explosive growth and rapidly growing technology sophistication have turned bug detection into an urgent issue in the modern gaming industry. Poor bug detection may even destroy the player experience and the commercial success. Traditional bug detection approaches are mostly manual-based, and they are inefficient, costly, and ineffective for the exploding size and sophistication of game environments. In this paper, a survey and technical analysis are conducted of four representative game bug detection approaches based on public dataset validation, including text, image, static analysis, and dynamic analysis approaches. The analysis shows that different game types may benefit from different approaches, which have their own pros and cons for different bug types. Meanwhile, different combination approaches of these four types could also reduce the false positive rate effectively. The study provides practical hints for the game quality assurance team. They could trade off the accuracy, latency, and domain applicability.