{"title":"Hyper-Erlang Software Reliability Model","authors":"H. Okamura, T. Dohi","doi":"10.1109/PRDC.2008.20","DOIUrl":null,"url":null,"abstract":"This paper proposes a hyper-Erlang software reliability model (HErSRM) in the framework of non-homogeneous Poisson process (NHPP) modeling. The proposed HErSRM is a generalized model which contains some existing NHPP-based SRMs like Goel-Okumoto SRM and Delayed S-shaped SRM, and can represent a variety of software fault-detection patterns. Such characteristics are useful to solve the model selection problem arising in the practical use of NHPP-based SRMs. More precisely, we discuss the statistical inference of HErSRM based on the EM (expectation-maximization) algorithm. In numerical experiments, we show that the HErSRM outperforms conventional NHPP-based SRMs with respect to fitting ability.","PeriodicalId":369064,"journal":{"name":"2008 14th IEEE Pacific Rim International Symposium on Dependable Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 14th IEEE Pacific Rim International Symposium on Dependable Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRDC.2008.20","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
This paper proposes a hyper-Erlang software reliability model (HErSRM) in the framework of non-homogeneous Poisson process (NHPP) modeling. The proposed HErSRM is a generalized model which contains some existing NHPP-based SRMs like Goel-Okumoto SRM and Delayed S-shaped SRM, and can represent a variety of software fault-detection patterns. Such characteristics are useful to solve the model selection problem arising in the practical use of NHPP-based SRMs. More precisely, we discuss the statistical inference of HErSRM based on the EM (expectation-maximization) algorithm. In numerical experiments, we show that the HErSRM outperforms conventional NHPP-based SRMs with respect to fitting ability.