A pragmatic ensemble learning approach for effective software effort estimation

The immense increase in software technology has resulted in the convolution of software projects. Software effort estimation is fundamental to commence any software project and inaccurate estimation may lead to several complications and setbacks for present and future projects. Several techniques have been following for ages of the software effort estimation. As the application of software is extensively increased in its size and complexity, the traditional methods aren’t adequate to meet the requirements. To achieve the accurate estimation of software effort, in this paper, a gradient boosting regressor model is proposed as a robust approach. The performance is compared with regression models such as stochastic gradient descent, K-nearest neighbor, decision tree, bagging regressor, random forest regressor, Ada-boost regressor, and gradient boosting regressor by employing COCOMO’81 containing 63 projects and CHINA of 499 projects. The regression models are evaluated by the evaluation metrics such as MAE, MSE, RMSE, and R². From the results, it is evident that the gradient boosting regressor model is performing well by obtaining an accuracy of 98% with COCOMO’81 and 93% with CHINA dataset. The proposed method significantly performs better than all regression models used in comparison with both the datasets.