Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports
Yanfu Yan, Nathan Cooper, Oscar Chaparro, Kevin Moran, Denys, Poshyvanyk

TL;DR
This paper presents JANUS, a novel approach using vision transformers and video alignment to improve duplicate detection in video-based GUI bug reports, significantly outperforming prior methods.
Contribution
JANUS introduces scene-learning with vision transformers and adaptive video frame alignment for more accurate duplicate detection in GUI bug videos.
Findings
Achieves 89.8% mRR and 84.7% mAP on benchmark
Outperforms prior work by around 9% in duplicate detection accuracy
Effectively captures subtle visual and textual patterns in GUI videos
Abstract
Video-based bug reports are increasingly being used to document bugs for programs centered around a graphical user interface (GUI). However, developing automated techniques to manage video-based reports is challenging as it requires identifying and understanding often nuanced visual patterns that capture key information about a reported bug. In this paper, we aim to overcome these challenges by advancing the bug report management task of duplicate detection for video-based reports. To this end, we introduce a new approach, called JANUS, that adapts the scene-learning capabilities of vision transformers to capture subtle visual and textual patterns that manifest on app UI screens - which is key to differentiating between similar screens for accurate duplicate report detection. JANUS also makes use of a video alignment technique capable of adaptive weighting of video frames to account for…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Web Data Mining and Analysis · Software Engineering Research
