Optimization of Software Defect Prediction using Supervised Machine Learning

Abstract

Software Defect Prediction (SDP) is a critical research area in software engineering that aims to identify defect-prone modules during the early stages of development. Accurate prediction of software defects enables efficient allocation of testing resources, reduces maintenance cost, and enhances overall system reliability. This study focuses on the optimization of Software Defect Prediction using supervised machine learning techniques. Various classification algorithms, including Logistic Regression, Decision Tree, Support Vector Machine (SVM), Random Forest, and Gradient Boosting, are implemented and comparatively analyzed. To enhance predictive performance, multiple optimization strategies such as feature selection, hyperparameter tuning using Grid Search with cross-validation, and class imbalance handling techniques like SMOTE are incorporated. The experimental evaluation is conducted using standard software defect datasets and performance metrics including Accuracy, Precision, and Recall. Results demonstrate that ensemble-based methods, particularly Random Forest and Gradient Boosting, outperform traditional classifiers in terms of generalization capability and robustness. The optimized framework significantly reduces false positives and improves defect detection rate. The proposed approach provides a scalable and efficient solution for early fault identification, supporting data-driven decision-making in modern software development environments. Future enhancements may include hybrid deep learning models and cross-project defect prediction to further improve predictive reliability.