Analyzing Effect of Ensemble Models on Multi-Layer Perceptron Network for Software Effort Estimation

Abstract

Effort Estimation is a very challenging task in the software development life cycle. Inaccurate estimations may cause client dissatisfaction and thereby, decrease the quality of the product. Considering the problem of software cost and effort estimation, it is conceivable to call attention to that the estimation procedure considers the qualities present in the data set, as well as the aspects of the environment in which the model is embedded. Existing literature have the instances where machine learning techniques have been used to estimate the effort required to develop any software. Yet it is quite uncertain for any particular model to perform well with all the data sets. In this paper, Multi-Layer Perceptron (MLPNN) and its ensembles are explored in order to improve the performance of software effort estimation process. Firstly, MLPNN, Ridge-MLPNN, Lasso-MLPNN, Bagging-MLPNN, and AdaBoost-MLPNN models are developed and, then, the performance of these models are compared on the basis of R2 score to find the best model fitting this dataset. Results obtained from the study demonstrate that the R2 score of AdaBoost-MLPNN is 82.213%, which is highest among all the models.