{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1878"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1878","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"AstroBug: automatic game bug detection using deep learning","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Azizi, Elham"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Zaman, Loutfouz"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08-01","date_published":"2023-08-01","updated_at":"2026-07-24T05:35:43Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1878","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zaman, Loutfouz"]},{"key":"dc:creator","label":"Author","values":["Azizi, Elham"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-03-17T15:39:07Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-03-17T15:39:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-08-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1878"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["AstroBug: automatic game bug detection using deep learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zaman, Loutfouz"],"dc:creator":["Azizi, Elham"],"dc:date.accessioned":["2025-03-17T15:39:07Z"],"dc:date.available":["2025-03-17T15:39:07Z"],"dc:date.issued":["2023-08-01"],"dc:description.abstract":["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."],"dc:identifier.uri":["https://hdl.handle.net/10155/1878"],"dc:language.iso":["en"],"dc:title":["AstroBug: automatic game bug detection using deep learning"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:43Z"}