Parameter tuning in KNN for software defect prediction: an empirical analysis

Modinat Abolore Mabayoje; Abdullateef Olwagbemiga Balogun; Hajarah Afor Jibril; Jelili Olaniyi Atoyebi; Hammed Adeleye Mojeed; Victor Elijah Adeyemo

doi:10.14710/jtsiskom.7.4.2019.121-126

DOI: https://doi.org/10.14710/jtsiskom.7.4.2019.121-126

Parameter tuning in KNN for software defect prediction: an empirical analysis

Modinat Abolore Mabayoje¹, Abdullateef Olwagbemiga Balogun¹

, Hajarah Afor Jibril¹, Jelili Olaniyi Atoyebi², Hammed Adeleye Mojeed¹, Victor Elijah Adeyemo¹

¹Department of Computer Science, University of Ilorin, Nigeria

²Department of Computer Science and Engineering, Obafemi Awolowo University, Nigeria

Received: 27 Jan 2019; Revised: 31 Jul 2019; Accepted: 10 Aug 2019; Available online: 3 Oct 2019; Published: 31 Oct 2019.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

BibTex Citation Data :

@article{JTSISKOM13262,
    author = {Modinat Abolore Mabayoje and Abdullateef Olwagbemiga Balogun and Hajarah Afor Jibril and Jelili Olaniyi Atoyebi and Hammed Adeleye Mojeed and Victor Elijah Adeyemo},
    title = {Parameter tuning in KNN for software defect prediction: an empirical analysis},
    journal = {Jurnal Teknologi dan Sistem Komputer},
  volume = {7},
    number = {4},
    year = {2019},
    keywords = {software defect prediction; parameter tuning; k-nearest neighbor; distance function; distance weighting},
    abstract = {Software Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.},
   issn = {2338-0403},   pages = {121--126}  doi = {10.14710/jtsiskom.7.4.2019.121-126},
    url = {https://jtsiskom.undip.ac.id/article/view/13262}
}

Citation Format:

Abstract

Software Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.

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Keywords: software defect prediction; parameter tuning; k-nearest neighbor; distance function; distance weighting

Funding: University of Ilorin; Obafemi Awolowo University

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Section: Original Research Articles

Language : EN

In Volume 7, Issue 4, Year 2019 (October 2019)

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Parameter tuning in KNN for software defect prediction: an empirical analysis

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