Software cost estimation using computational intelligence techniques

This paper presents computational intelligence techniques for software cost estimation. We proposed a new recurrent architecture for genetic programming (GP) in the process. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are implemented. We also performed GP based feature selection. The efficacy of these techniques viz multiple linear regression, polynomial regression, support vector regression, classification and regression tree, multivariate adaptive regression splines, multilayer feedforward neural network, radial basis function neural network, counter propagation neural network, dynamic evolving neuro-fuzzy inference system, tree net, group method of data handling and genetic programming has been tested on the International Software Benchmarking Standards Group (ISBSG) release 10 dataset. Ten-fold cross validation is performed throughout the study. The results obtained from our experiments indicate that new recurrent architecture for genetic programming outperformed all the other techniques.