文章基本信息

标题：An Enhanced Method Of Lms Parameter Estimation For Software Realiability Model
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作者：SWAMYDOSS D ; DR. G.M.KADHAR NAWAZ
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2013
卷号：2
期号：7
页码：2667
出版社：S&S Publications
摘要：A software reliability model specifies the form of a random process that describes the behavior ofsoftware failures with respect to time. In this work the author has taken a Non Homogenous Poisson softwarereliability model called Gompertz model to predict the reliability of the software. It is shown that the proposedmodel can be derived from the well-known statistical theory of extreme value and has the quite similar sympatricproperty to the classical Gompertz curve. We have applied the Gompertz software reliability model to assess thesoftware reliability and to predict the number of initial fault contents. The parameters used in this model areunknown, estimating this model parameter using an alternative approach of Least Mean Square estimation method.This new parameter estimation approach may function better than the existing estimation methods and is attractivein terms of goodness of fit, test based on information criteria and mean squared error. Software undergoes severalstages of testing before it is put into operation. In every stage of testing, modification and correction are made withthe hope of increasing reliability. All existing software reliability models are developed for the software productsthat are statically constructed normally by a company or institution that has the full control of the developmentprocess. The evolutional shift from the product-oriented software architecture to the Service Oriented Architecture(SOA) and Web Services (WS) invalids many techniques developed for traditional software. Hence in this work wehave considered web based application with alternative approach for parameter estimation in Gompertz softwarereliability model.
关键词：Software Reliability Model; Gompertz model; Least Mean Square Estimation; Web based Applications