科研成果详情

题名What Users Think about Predictive Analytics? A Survey on NFRs
作者
发表日期2020-08-01
会议名称28th IEEE International Requirements Engineering Conference (RE)
会议录名称Proceedings of the IEEE International Conference on Requirements Engineering
ISSN1090-705X
卷号2020-August
页码340-345
会议日期AUG 31-SEP 04, 2020
会议地点Zurich, SWITZERLAND
会议举办国SWITZERLAND
摘要

With the recent advancement in data science, Predictive Analytics (PA) functions have been built into many commercial products, which affects several 'non-functional' goals, including usability, performance, and transparency of the software, as well as privacy and well-being of the user. The direct and indirect consequences are yet to be understood better before the service providers take any further actions in response. In this work, a survey was conducted with a sample set of 153 respondents from U.S., on their acceptance of applications with PA. The result shows that many consumers recognize the benefit of PA features, but they are not without concerns about transparency, privacy, and personal well-being. Once users are highly concerned, they may choose not to use these features or even give up the products altogether. Based on the survey result, we have discussed how requirements engineering can help the stakeholders make better decisions related to PA adoption and design, and how RE tools can help address user concerns related to PA.

关键词acceptance consumer non-functional requirements predictive analytics survey
DOI10.1109/RE48521.2020.00045
URL查看来源
收录类别CPCI-S
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Information Systems & Computer Science, Software Engineering
WOS记录号WOS:000628527900036
Scopus入藏号2-s2.0-85093916871
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/9119
专题个人在本单位外知识产出
作者单位
1.California State University,Sacramento Dept. of Computer Science,Sacramento,United States
2.Tsinghua University,School of Software,Beijing,China
推荐引用方式
GB/T 7714
Yang, Jingwei,Liu, Lin. What Users Think about Predictive Analytics? A Survey on NFRs[C], 2020: 340-345.
条目包含的文件
条目无相关文件。
个性服务
查看访问统计
谷歌学术
谷歌学术中相似的文章
[Yang, Jingwei]的文章
[Liu, Lin]的文章
百度学术
百度学术中相似的文章
[Yang, Jingwei]的文章
[Liu, Lin]的文章
必应学术
必应学术中相似的文章
[Yang, Jingwei]的文章
[Liu, Lin]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。