优先考虑基于深度学习的视频分类器的测试案例
Yinghua Li1, Xueqi Dang1, Lei Ma2
1SnT Centre, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
Empirical software engineering
|September 9, 2024
概括
VRank是视频的新测试优先级方法,通过专注于可能被错误分类的视频测试案例来降低标签成本. 它有效地识别出有缺陷的视频,比现有方法更快.
科学领域:
- 软件工程 软件工程 软件工程
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 视频应用很普遍,但由于时间数据和大量数据,视频测试案例标签是昂贵的.
- 现有的测试优先级方法无法利用视频数据独特的时间信息.
- 有效地评估基于视频的系统的准确性需要解决标签成本和时间复杂性.
研究的目的:
- 介绍VRank,这是首个专门为视频测试输入设计的测试优先级方法.
- 为了减少与标记视频测试案例相关的成本和工作,以评估系统准确性.
- 提高识别错误分类的视频测试案例的效率,从而更早地检测系统故障.
主要方法:
- 开发了VRank,这是一种针对视频数据量身定制的新型测试优先级技术.
- 训练了一个排名模型来预测视频测试输入的错误分类概率,通过深度神经网络 (DNN) 分类器.
- 使用四种特征类型进行预测:时间特征 (TF),视频嵌入特征 (EF),预测特征 (PF) 和不确定性特征 (UF).
主要成果:
- VRank有效地根据其预测的错误分类概率优先考虑视频测试案例.
- 120名受试者的实证评估表明VRank的表现优于现有方法.
- VRank实现了显著的平均改善:自然数据集5.76%46.51%,噪音数据集4.26%53.56%.
结论:
- VRank是一种高效的视频输入测试优先级方法,性能优于传统方法.
- 该方法成功地解决了视频数据在测试案例优先排序中所带来的独特挑战.
- VRank提供了一种切实可行的解决方案,用于降低标签成本,加快基于视频的系统中的故障检测.
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