一句话：提出图文融合的众包移动测试报告选择工具 STIFA，通过聚类提升重复报告检测准确率。
众包移动测试因便捷高效而被广泛采用，但重复测试报告阻碍了其效率进一步提升。现有报告分析技术通常独立利用截图和文本描述，未能识别两类信息之间的关联。本文提出众包移动测试报告选择工具 STIFA，用于提取报告中的图像与文本特征信息，并建立图文融合的 bug 上下文；基于文本与图像融合分析结果，STIFA 进行聚类分析和报告选择。评估中，作者使用 STIFA 分析来自 2 个应用的 150 份报告。结果表明，STIFA 平均可提取 95.23% 的文本特征信息和 84.15% 的图像特征信息，重复报告检测准确率达到 87.64%。
Crowdsourced mobile testing has been widely used due to its convenience and high efficiency [10]. Crowdsourced workers complete testing tasks and record results in test reports. However, the problem of duplicate reports has prevented the efficiency of crowdsourced mobile testing from further improving. Existing crowdsourced testing report analysis techniques usually leverage screenshots and text descriptions independently, but fail to recognize the link between these two types of information. In this paper, we present a crowdsourced mobile testing report selection tool, namely STIFA, to extract image and text feature information in reports and establish an image-text-fusion bug context. Based on text and image fusion analysis results, STIFA performs cluster analysis and report selection. To evaluate, we employed STIFA to analyze 150 reports from 2 apps. The results show that STIFA can extract, on average, 95.23% text feature information and 84.15% image feature information. Besides, STIFA reaches an accuracy of 87.64% in detecting duplicate reports. The demo can be found at https://youtu.be/Gw6ptqyQbQY.
DOI 原文 ·
@inproceedings{paperbot5869,
title = {STIFA},
author = {Zhenfei Cao and Xu Wang and Shengcheng Yu and Yexiao Yun and Chunrong Fang},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3415300}
}