摘要
针对单一软件可靠性模型适应性不强和数据驱动模型稳定性较差的问题,本文选取3种典型软件可靠性模型作为基模型,利用极限学习机对基模型的预测结果进行加权优化,得到组合软件可靠性模型,实现经典软件可靠性模型和人工智能算法的有机结合。通过对3组失效数据进行仿真实验,并与单一模型、基于其他神经网络算法的组合模型以及数据驱动模型的预测结果进行对比,验证了本文模型能够有效地提升预测精度和模型的适应性。
To solve the problem of weak adaptability of single software reliability models and poor stability of data-driven models,this paper chooses three typical software reliability models as basic models,uses extreme learning machine to weigh and optimize the prediction results of basic models to obtain the combined software reliability model,which realizes the organic combination of classical software reliability models and artificial intelligence algorithm.Through simulation experiments on three sets of software failure data and comparison with the prediction results of single models,combinational models based on other neural network algorithms and data-driven model,it is verified that the combined software reliability model in this paper can effectively improve the prediction accuracy and model adaptability.
作者
李思雨
LI Si-yu(Software Testing Center,North China Institute of Computing Technology,Beijing 100083,China)
出处
《计算机与现代化》
2019年第11期44-48,共5页
Computer and Modernization
关键词
软件可靠性模型
组合模型
神经网络
极限学习机
预测精度
software reliability models
combinational models
neural network
extreme learning machine
prediction accuracy
