{"title":"用随机模型拟合软件故障数据","authors":"I.P. Schagen , M.M. Sallih","doi":"10.1016/0143-8174(87)90010-2","DOIUrl":null,"url":null,"abstract":"<div><p>Examination of data on software failure reveals that models which assume continuous reliability growth are not accurate. Simulation models incorporating more realistic assumptions have given results in closer accord with the data. Both the simulation models and the real data can be fitted by a simple logistic model.</p></div>","PeriodicalId":101070,"journal":{"name":"Reliability Engineering","volume":"17 2","pages":"Pages 111-126"},"PeriodicalIF":0.0000,"publicationDate":"1987-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0143-8174(87)90010-2","citationCount":"3","resultStr":"{\"title\":\"Fitting software failure data with stochastic models\",\"authors\":\"I.P. Schagen , M.M. Sallih\",\"doi\":\"10.1016/0143-8174(87)90010-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Examination of data on software failure reveals that models which assume continuous reliability growth are not accurate. Simulation models incorporating more realistic assumptions have given results in closer accord with the data. Both the simulation models and the real data can be fitted by a simple logistic model.</p></div>\",\"PeriodicalId\":101070,\"journal\":{\"name\":\"Reliability Engineering\",\"volume\":\"17 2\",\"pages\":\"Pages 111-126\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1987-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/0143-8174(87)90010-2\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Reliability Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/0143817487900102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reliability Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0143817487900102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fitting software failure data with stochastic models
Examination of data on software failure reveals that models which assume continuous reliability growth are not accurate. Simulation models incorporating more realistic assumptions have given results in closer accord with the data. Both the simulation models and the real data can be fitted by a simple logistic model.
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