Bagging Predictors for Estimation of Software Project Effort

This paper proposes and investigates the use of bagging predictors to improve performance of regression methods for estimation of the effort to develop software projects. We have applied bagging to M5P/regression trees, M5P/model trees, multi-layer perceptron (MLP), linear regression and support vector regression (SVR). This article reports on the influence of bagging on the performance of each of these regression methods in the estimation of the effort of software projects. Experiments carried out using a dataset of software projects from NASA show that bagging is able to significantly improve performance of regression methods in this task. Moreover, we show that bagging with M5P/model trees considerably outperforms previous results reported in the literature obtained by both linear regression and RBF networks. It is also shown that bagging with M5P/model trees obtains results comparable to those of SVR, with the advantage of producing more interpretable results.