计算机科学 ›› 2009, Vol. 36 ›› Issue (9): 135-138.

• 软件工程与数据库技术 • 上一篇    下一篇

一种软件可靠性增长模型选择与综合方法

冯光成,顾庆,陈翔,陈道蓄   

  1. (南京大学计算机科学与技术系计算机软件新技术国家重点实验室 南京 210093)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家“863”项目(编号:2006AA01Zl77),国家自然科学基金NSFC(编号:60873027),江苏省自然科学基金基础研究项目(编号:BK2006115)资助

Software Reliability Growth Model Selection and Composition Method

FENG Guang-chen, GU Qing, CHEN Xiang, CHEN Dao-xu   

  • Online:2018-11-16 Published:2018-11-16

摘要: 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。

关键词: 软件可靠性,可靠性增长模型,模型选择与综合,神经网络

Abstract: 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. I}he method was applied in a case study using failure data from some real world software projects and the experiment indicates its efficiency.

Key words: Software reliability, Software Reliability Growth Model, Model selection and compose, Neural network

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