Class Point Approach for Software Effort Estimation Using Various Support Vector Regression Kernel Methods

Accurate effort estimation in early stage of software development life cycle is a major challenge for many software industries. The use of various optimization techniques helps in improving the effort estimation accuracy. The Support Vector Regression (SVR) is one of different soft-computing techniques, that helps in getting optimal estimated values. The idea of SVR is based upon the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. Further, the SVR kernel methods can be applied in transforming the input data and then based on these transformations, an optimal boundary between the possible outputs can be obtained. The main objective of the research work carried out in this paper is to estimate the software effort using class point approach. Then, an attempt has been made to optimize the results obtained from class point analysis using various SVR kernel methods to achieve better prediction accuracy. A performance comparison of the models obtained using various SVR kernel methods has also been presented in order to highlight performance achieved by each method.