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Vol. 9, No. 4, November 2024 (Article in Progress)

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Improving Software Defect Prediction With a Combination of Feature Selection Based On Ant Colony Optimization and Ensemble Technique

https://doi.org/10.22219/kinetik.v9i4.2038
Windi Eka Yulia Retnani
Muhammad 'Ariful Furqon
Juni Setiawan
Jember university

Corresponding Author(s) : Windi Eka Yulia Retnani

windi.ilkom@unej.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 9, No. 4, November 2024 (Article in Progress)

Abstract

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. The research utilizes 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. Data preprocessing involved handling class
imbalances using the SMOTE technique and transforming
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.

Keywords

Software Defect Ant Colony Optimization SMOTE Ensemble
Retnani, W. E. Y., Furqon, M. ’Ariful ., & Setiawan, J. (2024). Improving Software Defect Prediction With a Combination of Feature Selection Based On Ant Colony Optimization and Ensemble Technique. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 9(4). https://doi.org/10.22219/kinetik.v9i4.2038
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