Prophecy of software maintenance effort with univariate and multivariate approach: By using support vector machine learning techniquewith radial Basis Kernel Function

Abstract

As seen in many studies the relationship of object oriented matrices of the software and the calculated maintenance effort metric is very complicated, complex and nonlinear in nature. So with this kind of behavior, we can have got a research area where we can work upon to minimize the maintenance effort which can be used to develop and deploy models and systems for the forecasting of software maintenance effort of the software. In our work we are using Support Vector Machine for the regression for forecasting of software maintenance factor with the Univariate and Multivariate approach. For better performance of Support Vector Machine we are using one of the popular kernel function, i.e. Radial Basis Kernel Function. We are using one commercial software products QUES Dataset (Quality Evaluation System) for the work. Dependent variable in our work is maintenance effort and independent variables are OO metrics. We will verify our work on Univariate and Multivariate premise.