Neural networks for pbkp_redicting the duration of new software projects

Abstract:

The duration of software development projects has become a competitive issue: only 39% of them are finished on time relative to the duration planned originally. The techniques for pbkp_redicting project duration are most often based on expert judgment and mathematical models, such as statistical regression or machine learning. The contribution of this study is to investigate whether or not the duration pbkp_rediction accuracy obtained with a multilayer feedforward neural network model, also called a multilayer perceptron (MLP), and with a radial basis function neural network (RBFNN) model is statistically better than that obtained by a multiple linear regression (MLR) model when functional size and the maximum size of the team of developers are used as the independent variables. The three models mentioned above are trained and tested by pbkp_redicting the duration of new software development projects with a set of projects from the International Software Benchmarking Standards Group (ISBSG) release 11. Results based on absolute residuals, Pred(l) and a Friedman statistical test show that pbkp_rediction accuracy with the MLP and the RBFNN is statistically better than with the MLR model.

Año de publicación:

2015

Keywords:

• Multilayer feedforward neural network
• Software project duration pbkp_rediction
• Radial basis function neural network

Fuente:

scopus