High software reliability is an important attribute of high-assurance systems. Software quality models yield timely predictions of quality indicators on a module-by-module basis, enabling one to focus on finding faults early in development. This paper introduces the Classification And Regression Trees (CART) algorithm to practitioners in high-assurance systems engineering. This paper presents practical lessons learned on building classification trees for software quality modeling, including an innovative way to control the balance between misclassification rates. A case study of a very large telecommunications system used CART to build software quality models. The models predicted whether or not modules would have faults discovered by customers, based on various sets of software product and process metrics as independent variables. We found that a model based on two software product metrics had comparable accuracy to a model based on forty product and process metrics.
Abstract-Software Defined Networking is a paradigmshifting technology in the field of computer networking. It empowers network administrators by giving them the ability to manage the network services through abstraction of the low-level network functionalities. This technology simplifies networking and makes it programmable. This paper presents an implementation of this new paradigm of networking, which can replace the currently existing legacy networking infrastructure to provide more control over the network, perform a better analysis of the network operation and hence program the network according to the needs of the network administrator. This implementation also empowers the network administrators to provide Quality of Service to its users that are connected to the network and uses the services of the network. Therefore, it benefits both the network administrator and the users. Also, the ping latency in the network is reduced by 5-10%, and the number of packets in is reduced by 60-70% in the solution developed depending on the size of the network.
High software r eliability is an important attribute of high-assurance systems. Software quality models yield timely predictions of reliability indicators on a module-by-module basis, enabling one to focus on nding faults early in development. This paper introduces the Classi cation And Regression Trees cart algorithm to practitioners in high-assurance systems engineering. This paper presents practical lessons-learned o n building classi cation trees for software quality modeling, including an innovative way to control the balance b etween misclassi cation rates. A case study of a very large telecommunications system used cart to build software quality models. The models predicted whether or not modules would have faults discovered by customers, based on various sets of software p r oduct and process metrics as independent variables. We found that a model based on two software p r oduct metrics had comparable accuracy to a model based on forty product and process metrics.
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