GameQualityAssurance(QA)currently relies heavily onmanual testing,a process that is both costly and time-consuming.Traditional script-and log-based automation tools are limited in their ability to detect unpredictable...GameQualityAssurance(QA)currently relies heavily onmanual testing,a process that is both costly and time-consuming.Traditional script-and log-based automation tools are limited in their ability to detect unpredictable visual bugs,especially those that are context-dependent or graphical in nature.As a result,many issues go unnoticed during manual QA,which reduces overall game quality,degrades the user experience,and creates inefficiencies throughout the development cycle.This study proposes two approaches to address these challenges.The first leverages a Large Language Model(LLM)to directly analyze gameplay videos,detect visual bugs,and automatically generate QA reports in natural language.The second approach introduces a pipeline method:first generating textual descriptions of visual bugs in game videos using the ClipCap model,then using those descriptions as input for the LLM to synthesize QA reports.Through these two multi-faceted approaches,this study evaluates the feasibility of automated game QA systems.To implement this system,we constructed a visual bug database derived from real-world game cases and fine-tuned the ClipCap model for the game video domain.Our proposed approach aims to enhance both efficiency and quality in game development by reducing the burden of manual QA while improving the accuracy of visual bug detection and ensuring consistent,reliable report generation.展开更多
基金supported by a grant from the Korea Creative Content Agency,funded by the Ministry of Culture,Sports and Tourism of the Republic of Korea in 2025,for the project,“Development of AI-based large-scale automatic game verification technology to improve game production verification efficiency for small and medium-sized game companies”(RS 2024-00393500).
文摘GameQualityAssurance(QA)currently relies heavily onmanual testing,a process that is both costly and time-consuming.Traditional script-and log-based automation tools are limited in their ability to detect unpredictable visual bugs,especially those that are context-dependent or graphical in nature.As a result,many issues go unnoticed during manual QA,which reduces overall game quality,degrades the user experience,and creates inefficiencies throughout the development cycle.This study proposes two approaches to address these challenges.The first leverages a Large Language Model(LLM)to directly analyze gameplay videos,detect visual bugs,and automatically generate QA reports in natural language.The second approach introduces a pipeline method:first generating textual descriptions of visual bugs in game videos using the ClipCap model,then using those descriptions as input for the LLM to synthesize QA reports.Through these two multi-faceted approaches,this study evaluates the feasibility of automated game QA systems.To implement this system,we constructed a visual bug database derived from real-world game cases and fine-tuned the ClipCap model for the game video domain.Our proposed approach aims to enhance both efficiency and quality in game development by reducing the burden of manual QA while improving the accuracy of visual bug detection and ensuring consistent,reliable report generation.
