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A Feature Selection-Based K-NN Model for Fast Software Defect Prediction

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Computational Science and Its Applications – ICCSA 2022 Workshops (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13380))

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Abstract

Software Defect Prediction (SDP) is an advanced technological method of predicting software defects in the software development life cycle. Various research works have been previously being done on SDP but the performance of these methods varied from several datasets, hence, making them inconsistent for SDP in the unknown software project. But the hybrid technique using feature selection enabled with machine learning for SDP can be very efficient as it takes the advantage of various methods to come up with better prediction accuracy for a given dataset when compared with an individual classifier. The major issues with individual ML-based models for SDP are the long detection time, vulnerability of the software project, and high dimensionality of the feature parameters. Therefore, this study proposes a hybrid model using a feature selection enabled Extreme Gradient Boost (XGB) classifier to address these mentioned challenges. The cleaned NASA MDP datasets were used for the implementation of the proposed model, and various performance metrics like F-score, accuracy, and MCC were used to reveal the performance of the model. The results of the proposed model when compared with state-of-the-art methods without feature selection perform better in terms of the metrics used. The results reveal that the proposed model outperformed all other prediction techniques.

A. E. Adeniyi and M. K. Abiodun—Landmark University SDG 4 (Quality Education)

M. K. Abiodun—Landmark University SDG 16 (Peace and Justice, Strong Institution)

A. E. Adeniyi—Landmark University SDG 11 (Sustainable Cities and Communities)

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Author information

Authors and Affiliations

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

    Joseph Bamidele Awotunde & Moses Kazeem Abiodun

  2. Department of Computer Science and Communication, Ostfold University College, Halden, Norway

    Sanjay Misra & Abidemi Emmanuel Adeniyi

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

    Moses Kazeem Abiodun

  4. Amity Institute of Information Technology, Amity University, Jaipur, Rajasthan, India

    Manju Kaushik

  5. Department of Computer Science, Baze University, Abuja, Nigeria

    Morolake Oladayo Lawrence

Authors
  1. Joseph Bamidele Awotunde
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  2. Sanjay Misra
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  3. Abidemi Emmanuel Adeniyi
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  4. Moses Kazeem Abiodun
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  5. Manju Kaushik
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  6. Morolake Oladayo Lawrence
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Corresponding author

Correspondence to Joseph Bamidele Awotunde .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Østfold University College, Halden, Norway

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  5. University of Cagliari, Cagliari, Italy

    Chiara Garau

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Awotunde, J.B., Misra, S., Adeniyi, A.E., Abiodun, M.K., Kaushik, M., Lawrence, M.O. (2022). A Feature Selection-Based K-NN Model for Fast Software Defect Prediction. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13380. Springer, Cham. https://doi.org/10.1007/978-3-031-10542-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-10542-5_4

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