AstroBug: automatic game bug detection using deep learning

Traditional methods of video game bug detection, such as manual testing have been effective, but they can also be time-consuming and costly. While Automated Bug Detection (ABD) techniques hold great promise for improving testing, they still face several challenges that need to be addressed to be effective in practice. In this work, we have introduced a new framework to detect perceptual bugs using a Long Short-Term Memory (LSTM) network, which detects bugs in games as anomalies. The detected buggy frames are then clustered to determine the category of the manifested bug. The framework was evaluated on two First Person Shooter (FPS) games. We further enhanced the framework by implementing Reinforcement Learning (RL) agent to autonomously gather datasets, effectively addressing the need for human players to collect data and manually browse through games. The enhancement was performed on a Role-Playing Game (RPG). The outcomes obtained validate the effectiveness of the framework.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Azizi, Elham

Advisor dc:contributor.advisor

• Zaman, Loutfouz

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1878

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1878