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Software Defect Prediction Analysis Using Machine Learning Techniques

Author

Listed:
  • Aimen Khalid

    (Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan
    These authors contributed equally to this work.)

  • Gran Badshah

    (Department of Computer Science, College of Computer Science, King Khalid University Abha, Abha 61413, Saudi Arabia
    These authors contributed equally to this work.)

  • Nasir Ayub

    (Department of Software Engineering, Faculty of Computing, Capital University of Science and Technology, Islamabad 44000, Pakistan
    These authors contributed equally to this work.)

  • Muhammad Shiraz

    (Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan
    These authors contributed equally to this work.)

  • Mohamed Ghouse

    (Department of Computer Science, College of Computer Science, King Khalid University Abha, Abha 61413, Saudi Arabia
    These authors contributed equally to this work.)

Abstract

There is always a desire for defect-free software in order to maintain software quality for customer satisfaction and to save testing expenses. As a result, we examined various known ML techniques and optimized ML techniques on a freely available data set. The purpose of the research was to improve the model performance in terms of accuracy and precision of the dataset compared to previous research. As previous investigations show, the accuracy can be further improved. For this purpose, we employed K-means clustering for the categorization of class labels. Further, we applied classification models to selected features. Particle Swarm Optimization is utilized to optimize ML models. We evaluated the performance of models through precision, accuracy, recall, f-measure, performance error metrics, and a confusion matrix. The results indicate that all the ML and optimized ML models achieve the maximum results; however, the SVM and optimized SVM models outperformed with the highest achieved accuracy, 99% and 99.80%, respectively. The accuracy of NB, Optimized NB, RF, Optimized RF and ensemble approaches are 93.90%, 93.80%, 98.70%, 99.50%, 98.80% and 97.60, respectively. In this way, we achieve maximum accuracy compared to previous studies, which was our goal.

Suggested Citation

  • Aimen Khalid & Gran Badshah & Nasir Ayub & Muhammad Shiraz & Mohamed Ghouse, 2023. "Software Defect Prediction Analysis Using Machine Learning Techniques," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5517-:d:1103190
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    References listed on IDEAS

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    1. Elena N. Akimova & Alexander Yu. Bersenev & Artem A. Deikov & Konstantin S. Kobylkin & Anton V. Konygin & Ilya P. Mezentsev & Vladimir E. Misilov, 2021. "A Survey on Software Defect Prediction Using Deep Learning," Mathematics, MDPI, vol. 9(11), pages 1-14, May.
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