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SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12254))

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Abstract

Class imbalance is a prevalent problem in machine learning which affects the prediction performance of classification algorithms. Software Defect Prediction (SDP) is no exception to this latent problem. Solutions such as data sampling and ensemble methods have been proposed to address the class imbalance problem in SDP. This study proposes a combination of Synthetic Minority Oversampling Technique (SMOTE) and homogeneous ensemble (Bagging and Boosting) methods for predicting software defects. The proposed approach was implemented using Decision Tree (DT) and Bayesian Network (BN) as base classifiers on defects datasets acquired from NASA software corpus. The experimental results showed that the proposed approach outperformed other experimental methods. High accuracy of 86.8% and area under operating receiver characteristics curve value of 0.93% achieved by the proposed technique affirmed its ability to differentiate between the defective and non-defective labels without bias.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, 1515, Nigeria

    Abdullateef O. Balogun, Fatimah B. Lafenwa-Balogun, Hammed A. Mojeed, Abimbola G. Akintola, Amos O. Bajeh & Fatimah E. Usman-Hamza

  2. School of Built Environment, Engineering and Computing, Leeds Beckett University, Headingley Campus, Leeds, LS6 3QS, UK

    Victor E. Adeyemo

  3. Department of Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria

    Oluwatobi N. Akande

Authors
  1. Abdullateef O. Balogun
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  2. Fatimah B. Lafenwa-Balogun
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  3. Hammed A. Mojeed
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  4. Victor E. Adeyemo
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  5. Oluwatobi N. Akande
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  6. Abimbola G. Akintola
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  7. Amos O. Bajeh
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  8. Fatimah E. Usman-Hamza
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Corresponding author

Correspondence to Oluwatobi N. Akande .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Balogun, A.O. et al. (2020). SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_45

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  • DOI: https://doi.org/10.1007/978-3-030-58817-5_45

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