Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.
With the increased expectation of faster and more modern software products for the digital market, providing accurate, qualitative, costeffective and time-bound software is crucial. Effective Software Project planning and Estimations are vital steps for developing a software product. The most precise effort in person-months or person-hours is predicted by software effort estimate. Inaccurate Estimations of effort provided by several estimation techniques have led to complications in the implementation of project, budget and schedule. Regression methods were the most common statistical method used for predicting modelling. Ensemble approaches use multiple learning algorithms to provide a better predictive model than individual learners. Our proposed scheme is stacked ensemble technique that used Decision Tree, Principal Components Regression (PCR), Random Forest, NeurelNet, glmnet, XGBoost, Earth and Support Vector Machine as base ensembled regression techniques. PCR as a Super learner was shown to be the best technique for assessing effort after extensive testing with a variety of super learners. Estimators were evaluated using the Mean Absolute Error (MAE), the Root Mean Squared Error (RMSE), the Mean Squared Error (MSE), the percentage of close approximations within 25 Percent of the true values (PRED (25)), and the R-squared coefficients.
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