一种软件可靠性增长模型选择与综合方法

Software Reliability Growth Model Selection and Composition Method

软件可靠性增长模型可以预测软件在将来某个时刻的可靠性,以此作为软件是否发布的依据。而目前常见的各种模型对不同失效数据集的预测能力并不一致。提出了一种软件可靠性增长模型选择和应用的框架,利用可靠性模型评价准则,对特定的失效数据集选择优选模型集,根据优选模型集利用神经网络较好的学习预测能力计算可靠性。利用此方法对实际软件项目中的失效数据进行了分析,并验证了它的有效性。

Software reliability growth models are used for the prediction of software reliability which can decided whether software could make release. At present, all kinds of common models＇ predict ability is inconsistent. We proposed a framework for software reliability growth model selection and application. The method provided guidelines on how to select better model set on the given failure data set, and then used these models to predict reliability with neural network. The method was applied in a case study using failure data from some real world software projects and the experiment indicates its efficiency.