文章基本信息

标题：IMPROVING SOFTWARE RELIABILITY GROWTH MODEL SELECTION RANKING USING PARTICLE SWARM OPTIMIZATION
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作者：LIANG FUH ONG ; MOHD ADHAM ISA ; DAYANG N. A. JAWAWI 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：1
出版社：Journal of Theoretical and Applied
摘要：Reliability of software always related to software failures and a number of software reliability growth models (SRGMs) have been proposed past few decades to predict software reliability. Different characteristics of SRGM leading to the study and practices of SRGM selection for different domains. Appropriate model must be chosen for suitable domain in order to predict the occurrence of the software failures accurately then help to estimate the overall cost of the project and delivery time. In this paper, particle swarm optimization (PSO) method is used to optimize a parameter estimation and distance based approach (DBA) is used to produce SRGM model selection ranking. The study concluded that the use of PSO for optimizing the SRGMs parameter has provided more accurate reliability prediction and improved model selection rankings. The model selection ranking methodology can facilitate a software developer to concentrate and analyze in making a decision to select suitable SRGM during testing phases.
关键词：Software Reliability Prediction; Model Selection; Parameter Estimation; Particle Swarm Optimization; Distance Based Approach; Software Reliability Growth Model (SRGM)