Using Support Vector Regression for Web Development Effort Estimation

Abstract

The objective of this paper is to investigate the use of Support Vector Regression (SVR) for Web development effort estimation when using a cross-company data set. Four kernels of SVR were used, linear, polynomial, Gaussian and sigmoid and two preprocessing strategies of the variables were applied, namely normalization and logarithmic. The hold-out validation process was carried out for all the eight configurations using a training set and a validation set from the Tukutuku data set. Our results suggest that the predictions obtained with linear kernel applying a logarithmic transformation of variables (LinLog) are significantly better than those obtained with the other configurations. In addition, SVR has been compared with the traditional estimation techniques, such as Manual StepWise Regression, Case-Based Reasoning, and Bayesian Networks. Our results suggest that SVR with LinLog configuration can provide significantly superior prediction accuracy than other techniques.