<![CDATA[Software defect prediction plays a vital role in enhancing software quality and minimizing maintenance costs. This study aims to improve software defect prediction by employing a combination of Ant Colony Optimization (ACO) for feature selection and ensemble techniques, particularly Gradient Boosting. This research utilized three NASA MDP datasets: MC1, KC1, and PC2, to evaluate the performance of four machine learning algorithms: Random Forest, Support Vector Machine (SVM), Decision Tree, and Naïve Bayes. The data preprocessing comprised handling class imbalance using SMOTE and converting categorical data into numerical representations. The results indicate that the integration of ACO and Gradient Boosting significantly enhances the accuracy of all four algorithms. Notably, the Random Forest algorithm achieved the highest accuracy of 99% on the MC1 dataset. The findings suggest that combining ACO-based feature selection with ensemble techniques can effectively boost the performance of software defect prediction models, offering a robust approach for early detection of potential software defects and contributing to improved software reliability and efficiency.]]>
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