Software Cost Estimation through Stacking Ensemble Approach: An Evaluation Study

Abstract

Accurately estimating software development costs is paramount for software project success. Global attention has focused on addressing inaccuracies in software cost estimating models leading to numerous approaches. Although machine learning algorithm techniques have witnessed improvement, the challenge of varying bias and variance from weaker learners persists, leading to the adoption of ensemble learning techniques for increased accuracy. This paper extends previous work on ensemble learning Software Cost Estimation (SCE) approach through the use of a Stacking Ensemble Learning Model for SCE (SELM-SCE) to improve the accuracy of software cost estimation. A combination of unsupervised and supervised machine learning methods were used on software projects. Two datasets (Desharnais and Maxwell) for software cost estimation were used for the model training and validation using cross-validation techniques. The proposed SELM-SCE model developed was subjected to two training models: K-Means and SMOTE technique to solve the problem of overfitting and poor performance caused by data imbalance. The performance of the proposed SELM-SCE model was first evaluated against three regression performance metrics: coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE). The results showed that the SMOTE SELM-SCE without unsupervised machine learning improves the accuracy of the model performance. The performance of SELM-SCE was compared with existing models using prediction accuracy, precision, recall, and F1-score classification performance metrics. The results from the SELM-SCE model showed remarkable improvement when compared to the existing model