Finding the Needle in a Haystack: Detecting Bug Occurrences in Gameplay Videos.
CoRR(2023)
Abstract
The presence of bugs in video games can bring significant consequences for
developers. To avoid these consequences, developers can leverage gameplay
videos to identify and fix these bugs. Video hosting websites such as YouTube
provide access to millions of game videos, including videos that depict bug
occurrences, but the large amount of content can make finding bug instances
challenging. We present an automated approach that uses machine learning to
predict whether a segment of a gameplay video contains the depiction of a bug.
We analyzed 4,412 segments of 198 gameplay videos to predict whether a segment
contains an instance of a bug. Additionally, we investigated how our approach
performs when applied across different specific genres of video games and on
videos from the same game. We also analyzed the videos in the dataset to
investigate what characteristics of the visual features might explain the
classifier's prediction. Finally, we conducted a user study to examine the
benefits of our automated approach against a manual analysis. Our findings
indicate that our approach is effective at detecting segments of a video that
contain bugs, achieving a high F1 score of 0.88, outperforming the current
state-of-the-art technique for bug classification of gameplay video segments.
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