COMPARISION OF ENSEMBLE LEARNING MODELS AND IMPACT OF DATA BALANCING TECHNIQUE FOR SOFTWARE EFFORT ESTIMATION

Abstract

Project management is a critical component of every software project's success. Estimating the cost and effort of software development at the outset of the project is one of the most important responsibilities in software project management. Estimating effort allows project managers to more effectively manage resources and activities. The primary purpose of this study was to construct and compare the usage of two common ensemble approaches (bagging and boosting) to improve estimator accuracy and to study the impact of Synthetic Minority Over-Sampling Technique for Regression (SMOTER) to predict effort estimation by using machine learning algorithms. Random forest, support vector regression, elastic net, decision tree regressor, linear regression, lasso regression, and ridge regression are some of the machine learning techniques we've implemented. For our study we used Albrecht, China, COCOMO81, Desharnais and Maxwell dataset. We also performed feature selection and considered only those features that have strong correlation with target feature i.e., effort. The two-performance metrics Mean Magnitude Relative Error (MMRE) and PRED(25) results demonstrate that utilising elastic net as the base learner for AdaBoost outperforms the other models and there is a significant decrease in error of each model after applying SMOTER.