Test Case Prioritization Using Adaptive Random Sequence with Category-Partition-Based Distance

Xiaofang Zhang, Xiaoyuan Xie, T. Chen
{"title":"Test Case Prioritization Using Adaptive Random Sequence with Category-Partition-Based Distance","authors":"Xiaofang Zhang, Xiaoyuan Xie, T. Chen","doi":"10.1109/QRS.2016.49","DOIUrl":null,"url":null,"abstract":"Test case prioritization schedules test cases in a certain order aiming to improve the effectiveness of regression testing. Random sequence is a basic and simple prioritization technique, while Adaptive Random Sequence (ARS) makes use of extra information to improve the diversity of random sequence. Some researchers have proposed prioritization techniques using ARS with white-box information, such as code coverage information, or with black-box information, such as string distances of the input data. In this paper, we propose new black-box test case prioritization techniques using ARS, and the diversity of test cases is assessed by category-partition-based distance. Our experimental studies show that these new techniques deliver higher fault-detection effectiveness than random prioritization, especially in the case of smaller ratio of failed test cases. In addition, in the comparison of different distance metrics, techniques with category-partition-based distance generally deliver better fault-detection effectiveness and efficiency, meanwhile in the comparison of different ordering algorithms, our ARS-based ordering algorithms usually have comparable fault-detection effectiveness but much lower computation overhead, and thus are much more cost-effective.","PeriodicalId":412973,"journal":{"name":"2016 IEEE International Conference on Software Quality, Reliability and Security (QRS)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Software Quality, Reliability and Security (QRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/QRS.2016.49","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24

Abstract

Test case prioritization schedules test cases in a certain order aiming to improve the effectiveness of regression testing. Random sequence is a basic and simple prioritization technique, while Adaptive Random Sequence (ARS) makes use of extra information to improve the diversity of random sequence. Some researchers have proposed prioritization techniques using ARS with white-box information, such as code coverage information, or with black-box information, such as string distances of the input data. In this paper, we propose new black-box test case prioritization techniques using ARS, and the diversity of test cases is assessed by category-partition-based distance. Our experimental studies show that these new techniques deliver higher fault-detection effectiveness than random prioritization, especially in the case of smaller ratio of failed test cases. In addition, in the comparison of different distance metrics, techniques with category-partition-based distance generally deliver better fault-detection effectiveness and efficiency, meanwhile in the comparison of different ordering algorithms, our ARS-based ordering algorithms usually have comparable fault-detection effectiveness but much lower computation overhead, and thus are much more cost-effective.
基于类别分区距离的自适应随机序列测试用例优先级排序
测试用例优先级是按照一定的顺序安排测试用例,目的是提高回归测试的有效性。随机序列是一种基本的、简单的优先排序技术,而自适应随机序列(ARS)利用额外的信息来提高随机序列的多样性。一些研究人员提出了将ARS与白盒信息(如代码覆盖信息)或黑盒信息(如输入数据的字符串距离)结合使用的优先级排序技术。在本文中,我们提出了新的使用ARS的黑盒测试用例优先级技术,并通过基于类别分区的距离来评估测试用例的多样性。我们的实验研究表明,这些新技术比随机优先级提供更高的故障检测效率,特别是在失败测试用例比例较小的情况下。此外,在不同距离度量的比较中,基于类别划分的距离技术通常具有更好的故障检测效果和效率,同时在不同排序算法的比较中,我们基于ars的排序算法通常具有相当的故障检测效果,但计算开销要小得多,因此成本效益更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信