OpTunedSMOTE: A novel model for automated hyperparameter tuning of SMOTE in software defect prediction

Abstract

Software Defect Prediction plays a crucial role in quality assurance by identifying potential defects early in the software development lifecycle. It is an essential aspect of modern software engineering that significantly contributes to improving software quality and reliability. It utilizes a variety of techniques, including machine learning algorithms like decision trees, support vector machines, and neural networks, to predict defects. A lot of research tried to improve the prediction accuracy but had problems with imbalanced data and hyperparameter tuning of the algorithms. To deal with this, we proposed a novel approach by tuning the hyperparameters of the Synthetic Minority Over-sampling Technique using the Tree-structured Parzen Estimator algorithm within the Optuna framework. Through an analysis of seventeen imbalanced datasets from a different public database, we compare our technique with existing SDP models using K-Nearest Neighbors, Multi-Layer Perceptron, Random Forest, Support Vector Machine, and Extreme Gradient Boosting classifiers. Our findings reveal that optimizing the Synthetic Minority Over-sampling Technique significantly improves the performance of SDP models, resulting in enhanced performance metrics. We have statistically validated our results using Friedman's test.