{"title":"提高Goel-Okumoto软件可靠性增长模型的预测性能","authors":"P.A. Keiller, T. Mazzuchi","doi":"10.1109/RAMS.2000.816292","DOIUrl":null,"url":null,"abstract":"In this paper, enhancement of the performance of the Goel-Okumoto Reliability Growth model is investigated using various smoothing techniques. The method of parameter estimation for the model is the maximum likelihood method. The evaluation of the performance of the model is judged by the relative error of the predicted number of failures over future time intervals relative to the number of failures eventually observed during the interval. The use of data analysis procedures utilizing the Laplace trend test are investigated. These methods test for reliability growth throughout the data and establish \"windows\" that censor early failure data and provide better model fits. The research showed conclusively that the data analysis procedures resulted in improvement in the models' predictive performance for 41 different sets of software failure data collected from software development labs in the United States and Europe.","PeriodicalId":178321,"journal":{"name":"Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Enhancing the predictive performance of the Goel-Okumoto software reliability growth model\",\"authors\":\"P.A. Keiller, T. Mazzuchi\",\"doi\":\"10.1109/RAMS.2000.816292\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, enhancement of the performance of the Goel-Okumoto Reliability Growth model is investigated using various smoothing techniques. The method of parameter estimation for the model is the maximum likelihood method. The evaluation of the performance of the model is judged by the relative error of the predicted number of failures over future time intervals relative to the number of failures eventually observed during the interval. The use of data analysis procedures utilizing the Laplace trend test are investigated. These methods test for reliability growth throughout the data and establish \\\"windows\\\" that censor early failure data and provide better model fits. The research showed conclusively that the data analysis procedures resulted in improvement in the models' predictive performance for 41 different sets of software failure data collected from software development labs in the United States and Europe.\",\"PeriodicalId\":178321,\"journal\":{\"name\":\"Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAMS.2000.816292\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAMS.2000.816292","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

本文研究了利用各种平滑技术增强Goel-Okumoto可靠性增长模型的性能。模型的参数估计方法是极大似然法。对模型性能的评价是通过预测未来时间间隔内的故障数量相对于该时间间隔内最终观察到的故障数量的相对误差来判断的。利用拉普拉斯趋势检验的数据分析程序的使用进行了研究。这些方法测试了整个数据的可靠性增长,并建立了“窗口”,以审查早期故障数据,并提供更好的模型拟合。研究最终表明,数据分析过程导致了模型预测性能的改进,这些预测性能来自美国和欧洲的软件开发实验室收集的41组不同的软件故障数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Enhancing the predictive performance of the Goel-Okumoto software reliability growth model
In this paper, enhancement of the performance of the Goel-Okumoto Reliability Growth model is investigated using various smoothing techniques. The method of parameter estimation for the model is the maximum likelihood method. The evaluation of the performance of the model is judged by the relative error of the predicted number of failures over future time intervals relative to the number of failures eventually observed during the interval. The use of data analysis procedures utilizing the Laplace trend test are investigated. These methods test for reliability growth throughout the data and establish "windows" that censor early failure data and provide better model fits. The research showed conclusively that the data analysis procedures resulted in improvement in the models' predictive performance for 41 different sets of software failure data collected from software development labs in the United States and Europe.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Learning to enhance reliability of electronic systems through effective modeling and risk assessment Evaluating the residual risks of infusing new technologies into NASA missions Advisory board - tools for reliability and maintainability practitioners Use of fault tree analysis for evaluation of system-reliability improvements in design phase Power-related failure mechanisms in the analysis of wireless system availability
×
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