一句话：提出 Tango，融合视觉与文本分析，高效检测基于屏幕录制视频的重复缺陷报告。
当缺陷表现在用户可见的 GUI 中时，用户越来越多地使用截图和屏幕录制来报告问题。在众包测试等大规模场景下，人工识别描述重复缺陷的视频既耗时又困难，而现有重复缺陷报告检测技术难以直接处理屏幕录制的图形特性。为此，论文提出 Tango，一种仅基于视频缺陷报告的重复检测技术，结合定制计算机视觉、光学字符识别与文本检索，同时利用视觉和文本信息。作者在六个 Android 应用的 180 个屏幕录制上构造了 4,860 个重复检测任务，评估多种 Tango 配置，并开展用户研究比较人工检测与使用 Tango 的代价。结果表明，最优配置在 83% 的任务中将目标重复视频排在前 2 位，平均可减少开发者超过 60% 的工作量，显示出较好的实用性。
When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. However, when such information is reported en masse, such as during crowd-sourced testing, managing these artifacts can be a time-consuming process. As the reporting of screen-recordings in particular becomes more popular, developers are likely to face challenges related to manually identifying videos that depict duplicate bugs. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. To overcome these challenges and aid developers in this task, this paper presents Tango, a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information. Tango combines tailored computer vision techniques, optical character recognition, and text retrieval. We evaluated multiple configurations of Tango in a comprehensive empirical evaluation on 4,860 duplicate detection tasks that involved a total of 180 screen-recordings from six Android apps. Additionally, we conducted a user study investigating the effort required for developers to manually detect duplicate video-based bug reports and compared this to the effort required to use Tango. The results reveal that Tango's optimal configuration is highly effective at detecting duplicate video-based bug reports, accurately ranking target duplicate videos in the top-2 returned results in 83% of the tasks. Additionally, our user study shows that, on average, Tango can reduce developer effort by over 60%, illustrating its practicality.
@inproceedings{paperbot10871,
title = {It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports},
author = {Nathan Cooper and Carlos Bernal-Cárdenas and Oscar Chaparro and Kevin Moran and Denys Poshyvanyk},
booktitle = {ICSE},
year = {2021},
doi = {10.1109/icse43902.2021.00091}
}