{"title":"基于计算智能的软件可靠性方法","authors":"Tamanna, O. Sangwan","doi":"10.1109/CONFLUENCE.2017.7943144","DOIUrl":null,"url":null,"abstract":"Accurate software reliability prediction with a single universal software reliability growth model is very difficult. In this ρ aper we reviewed different models which uses computational intelligence for the prediction purpose and describe how these techniques outperform conventional statistical models. Parameters, efficacy measures with methodologies are concluded in tabular form.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"108 1","pages":"171-176"},"PeriodicalIF":0.0000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computational intelligence based approaches to software reliability\",\"authors\":\"Tamanna, O. Sangwan\",\"doi\":\"10.1109/CONFLUENCE.2017.7943144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate software reliability prediction with a single universal software reliability growth model is very difficult. In this ρ aper we reviewed different models which uses computational intelligence for the prediction purpose and describe how these techniques outperform conventional statistical models. Parameters, efficacy measures with methodologies are concluded in tabular form.\",\"PeriodicalId\":6651,\"journal\":{\"name\":\"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence\",\"volume\":\"108 1\",\"pages\":\"171-176\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CONFLUENCE.2017.7943144\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CONFLUENCE.2017.7943144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computational intelligence based approaches to software reliability
Accurate software reliability prediction with a single universal software reliability growth model is very difficult. In this ρ aper we reviewed different models which uses computational intelligence for the prediction purpose and describe how these techniques outperform conventional statistical models. Parameters, efficacy measures with methodologies are concluded in tabular form.