Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports

• URL: http://arxiv.org/abs/2407.08610v1
• Date: Thu, 11 Jul 2024 15:48:36 GMT
• Title: Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports
• Authors: Yanfu Yan, Nathan Cooper, Oscar Chaparro, Kevin Moran, Denys Poshyvanyk,
• Abstract summary: Video-based bug reports are increasingly being used to document bugs for programs centered around a graphical user interface (GUI) 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. Janus also makes use of a video alignment technique capable of adaptive weighting of video frames to account for typical bug manifestation patterns.
• Score: 16.45808969240553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 typical bug manifestation patterns. In a comprehensive evaluation on a benchmark containing 7,290 duplicate detection tasks derived from 270 video-based bug reports from 90 Android app bugs, the best configuration of our approach achieves an overall mRR/mAP of 89.8%/84.7%, and for the large majority of duplicate detection tasks, outperforms prior work by around 9% to a statistically significant degree. Finally, we qualitatively illustrate how the scene-learning capabilities provided by Janus benefits its performance.

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