Weighted Proportional Sampling : AGeneralization for Sampling Strategies in Software Testing

G. Padilla, C.M. de Oca, I. Yen, F. Bastani
{"title":"Weighted Proportional Sampling : AGeneralization for Sampling Strategies in Software Testing","authors":"G. Padilla, C.M. de Oca, I. Yen, F. Bastani","doi":"10.1109/ICEEE.2006.251865","DOIUrl":null,"url":null,"abstract":"Current activities to measure the quality of software products rely on software testing. The size and complexity of software systems make it almost impossible to perform complete coverage testing. During the past several years, many techniques to improve the test effectiveness (i.e., the ability to find faults) have been proposed to address this issue. Two examples of such strategies are random testing and partition testing. Both strategies follow an input domain sampling to perform the testing process. The procedure and assumptions for selecting these points seem to be different for both strategies: random testing considers only the probability of each sub-domain (i.e. uniform sampling) while partition testing considers only the sampling rate of each sub-domain (i.e., proportional sampling). This paper describes a more general sampling strategy, named weighted proportional sampling strategy. This strategy unifies both strategies into a general model that encompasses both of them as special cases. This paper also proposes an optimization model to determine the number of sampled points depending on the sampling strategy","PeriodicalId":125310,"journal":{"name":"2006 3rd International Conference on Electrical and Electronics Engineering","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 3rd International Conference on Electrical and Electronics Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEE.2006.251865","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Current activities to measure the quality of software products rely on software testing. The size and complexity of software systems make it almost impossible to perform complete coverage testing. During the past several years, many techniques to improve the test effectiveness (i.e., the ability to find faults) have been proposed to address this issue. Two examples of such strategies are random testing and partition testing. Both strategies follow an input domain sampling to perform the testing process. The procedure and assumptions for selecting these points seem to be different for both strategies: random testing considers only the probability of each sub-domain (i.e. uniform sampling) while partition testing considers only the sampling rate of each sub-domain (i.e., proportional sampling). This paper describes a more general sampling strategy, named weighted proportional sampling strategy. This strategy unifies both strategies into a general model that encompasses both of them as special cases. This paper also proposes an optimization model to determine the number of sampled points depending on the sampling strategy
加权比例抽样:软件测试中抽样策略的推广
当前度量软件产品质量的活动依赖于软件测试。软件系统的规模和复杂性使得执行完整的覆盖测试几乎是不可能的。在过去的几年里,为了解决这个问题,已经提出了许多提高测试有效性(即发现错误的能力)的技术。这类策略的两个例子是随机测试和分区测试。这两种策略都遵循输入域采样来执行测试过程。对于两种策略,选择这些点的过程和假设似乎是不同的:随机测试只考虑每个子域的概率(即均匀抽样),而分区测试只考虑每个子域的抽样率(即比例抽样)。本文描述了一种更一般的抽样策略,称为加权比例抽样策略。该策略将这两种策略统一到一个通用模型中,该模型将这两种策略都作为特殊情况包含在内。本文还提出了一个根据采样策略确定采样点个数的优化模型
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:481959085
Book学术官方微信