随着社会的快速发展,许多能力成熟度模型已无法满足一些大型软件公司的需求,软件过程能力成熟度模型集成(Capability Maturity Model Integration,CMMI)应运而生。CMMI的普遍应用,给许多大型软件公司的高速运行带来了诸多便捷。文章通...随着社会的快速发展,许多能力成熟度模型已无法满足一些大型软件公司的需求,软件过程能力成熟度模型集成(Capability Maturity Model Integration,CMMI)应运而生。CMMI的普遍应用,给许多大型软件公司的高速运行带来了诸多便捷。文章通过研究CMMI的涵义、结构框架、表示和评估方法及重要性,旨在为CMMI高效使用提供可靠的理论依据和指导意见,使其更好地服务于诸多软件企业。展开更多
In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning ha...In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.展开更多
文摘随着社会的快速发展,许多能力成熟度模型已无法满足一些大型软件公司的需求,软件过程能力成熟度模型集成(Capability Maturity Model Integration,CMMI)应运而生。CMMI的普遍应用,给许多大型软件公司的高速运行带来了诸多便捷。文章通过研究CMMI的涵义、结构框架、表示和评估方法及重要性,旨在为CMMI高效使用提供可靠的理论依据和指导意见,使其更好地服务于诸多软件企业。
基金supported by the grants from Natural Science Foundation of China(Project No.61375045)the joint astronomic fund of the national natural science foundation of China and Chinese Academic Sinica(Project No.U1531242)Beijing Natural Science Foundation(4142030)
文摘In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.
