Module-order modeling using an evolutionary multi-objective optimization approach

T. Khoshgoftaar, Yi Liu, Naeem Seliya
{"title":"Module-order modeling using an evolutionary multi-objective optimization approach","authors":"T. Khoshgoftaar, Yi Liu, Naeem Seliya","doi":"10.1109/METRIC.2004.1357900","DOIUrl":null,"url":null,"abstract":"The problem of quality assurance is important for software systems. The extent to which software reliability improvements can be achieved is often dictated by the amount of resources available for the same. A prediction for risk-based rankings of software modules can assist in the cost-effective delegation of the limited resources. A module-order model (MOM) is used to gauge the performance of the predicted rankings. Depending on the software system under consideration, multiple software quality objectives may be desired for a MOM; e.g., the desired rankings may be such that if 20% of modules were targeted for reliability enhancements then 80% of the faults would be detected. In addition, it may also be desired that if 50% of modules were targeted then 100% of the faults would be detected. Existing works related to MOM(s) have used an underlying prediction model to obtain the rankings, implying that only the average, relative, or mean square errors are minimized. Such an approach does not provide an insight into the behavior of a MOM, the performance of which focusses on how many faults are accounted for by the given percentage of modules enhanced. We propose a methodology for building MOM (s) by implementing a multiobjective optimization with genetic programming. It facilitates the simultaneous optimization of multiple performance objectives for a MOM. Other prediction techniques, e.g., multiple linear regression and neural networks, cannot achieve multiobjective optimization for MOM(s). A case study of a high-assurance telecommunications software system is presented. The observed results show a new promise in the modeling of goal-oriented software quality estimation models.","PeriodicalId":261807,"journal":{"name":"10th International Symposium on Software Metrics, 2004. Proceedings.","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"10th International Symposium on Software Metrics, 2004. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/METRIC.2004.1357900","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

The problem of quality assurance is important for software systems. The extent to which software reliability improvements can be achieved is often dictated by the amount of resources available for the same. A prediction for risk-based rankings of software modules can assist in the cost-effective delegation of the limited resources. A module-order model (MOM) is used to gauge the performance of the predicted rankings. Depending on the software system under consideration, multiple software quality objectives may be desired for a MOM; e.g., the desired rankings may be such that if 20% of modules were targeted for reliability enhancements then 80% of the faults would be detected. In addition, it may also be desired that if 50% of modules were targeted then 100% of the faults would be detected. Existing works related to MOM(s) have used an underlying prediction model to obtain the rankings, implying that only the average, relative, or mean square errors are minimized. Such an approach does not provide an insight into the behavior of a MOM, the performance of which focusses on how many faults are accounted for by the given percentage of modules enhanced. We propose a methodology for building MOM (s) by implementing a multiobjective optimization with genetic programming. It facilitates the simultaneous optimization of multiple performance objectives for a MOM. Other prediction techniques, e.g., multiple linear regression and neural networks, cannot achieve multiobjective optimization for MOM(s). A case study of a high-assurance telecommunications software system is presented. The observed results show a new promise in the modeling of goal-oriented software quality estimation models.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
采用进化多目标优化方法的模阶建模
质量保证问题是软件系统的一个重要问题。软件可靠性改进的程度通常取决于可用资源的数量。对基于风险的软件模块排序的预测可以帮助对有限的资源进行经济有效的分配。使用模序模型(MOM)来衡量预测排名的性能。根据所考虑的软件系统,MOM可能需要多个软件质量目标;例如,期望的排名可能是这样的,如果20%的模块以可靠性增强为目标,那么80%的故障将被检测到。此外,如果50%的模块被定位,那么100%的故障将被检测到。与MOM(s)相关的现有工作使用了一个潜在的预测模型来获得排名,这意味着只有平均、相对或均方误差被最小化。这种方法不能洞察MOM的行为,MOM的性能关注的是在给定的增强模块百分比中有多少故障。我们提出了一种通过遗传规划实现多目标优化来构建MOM (s)的方法。它有助于同时优化MOM的多个性能目标。其他预测技术,如多元线性回归和神经网络,不能实现MOM(s)的多目标优化。介绍了一个高保证电信软件系统的实例研究。观察到的结果显示了面向目标的软件质量评估模型建模的新前景。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
The necessity of assuring quality in software measurement data Resource estimation for Web applications A survey on software estimation in the Norwegian industry A controlled experiment for evaluating a metric-based reading technique for requirements inspection Assessing usability through perceptions of information scent
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1