Software Reliability Prediction Model, And In The Power System Applications

With the rapid of the deployment of computer systems, people in the modern society are increasingly dependent on the hardware and software systems. When the demand for computer systems increases, the possibility of crises from computer failure will also increase. Therefore, the reliability of computer systems has become an important concern for our daily life. Numerous Software Reliability Prediction Models (SRPMs) for the software failure phenomenon have been developed to measure software reliability, and some of them are based on Nonhomogeneous Poisson Process (NHPP). However, some of the above assumptions of traditional SRPMs are not applicable in an actual software environment.Formally, a support vector machine (SVM) constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training datapoints of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. From the present studies it seems, SVM has been considerable attention in various fields, has become a major tool for research about classification and predication. Support vector machine based on statistical learning theory, it builds on the statistical theory of VC dimension and structural risk minimization theory. It attaches to the principles which can maximize the generalization ability of learning. This thesis concentrates in implementing SVM to software reliability prediction. It constructs the software reliability prediction model based on SVM in order to open a superior algorithm. Also, it applies this algorithm into the large software of power system. This paper introduces series of traditional software reliability prediction models.Then it analyzes how to use the SVM into the software reliability prediction work. This paper adopts the known software fault test data and introduces the modeling process of software reliability prediction model in detail. it compares the advantages and disadvantages between model based on SVM and traditional model. Through the simulation and analysis, it confirms the new model having a higher prediction accuracy, a faster velocity and effect, a stronger generalization compared to the standard BP algorithm. The new prediction method based on SVM does not like the traditional prediction method, it is without any assumptions. Therefore, this model has a certain adaptability and versatility. At last, it applies this software reliability algorithm into the large software of power system and gets a good predictive accuracy. This modeling ideas and methods provide some ideas and methods for reference to future researchers to develop better software reliability prediction model.