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Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

Author

Listed:
  • Himansu Das

    (School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India)

  • Sanjay Prajapati

    (School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India)

  • Mahendra Kumar Gourisaria

    (School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India)

  • Radha Mohan Pattanayak

    (School of Computer Science & Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India)

  • Abdalla Alameen

    (Computer Science Department, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Manjur Kolhar

    (Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

Abstract

A program’s bug, fault, or mistake that results in unintended results is known as a software defect or fault. Software flaws are programming errors due to mistakes in the requirements, architecture, or source code. Finding and fixing bugs as soon as they arise is a crucial goal of software development that can be achieved in various ways. So, selecting a handful of optimal subsets of features from any dataset is a prime approach. Indirectly, the classification performance can be improved through the selection of features. A novel approach to feature selection (FS) has been developed, which incorporates the Golden Jackal Optimization (GJO) algorithm, a meta-heuristic optimization technique that draws on the hunting tactics of golden jackals. Combining this algorithm with four classifiers, namely K-Nearest Neighbor, Decision Tree, Quadrative Discriminant Analysis, and Naive Bayes, will aid in selecting a subset of relevant features from software fault prediction datasets. To evaluate the accuracy of this algorithm, we will compare its performance with other feature selection methods such as FSDE (Differential Evolution), FSPSO (Particle Swarm Optimization), FSGA (Genetic Algorithm), and FSACO (Ant Colony Optimization). The result that we got from FSGJO is great for almost all the cases. For many of the results, FSGJO has given higher classification accuracy. By utilizing the Friedman and Holm tests, to determine statistical significance, the suggested strategy has been verified and found to be superior to prior methods in selecting an optimal set of attributes.

Suggested Citation

  • Himansu Das & Sanjay Prajapati & Mahendra Kumar Gourisaria & Radha Mohan Pattanayak & Abdalla Alameen & Manjur Kolhar, 2023. "Feature Selection Using Golden Jackal Optimization for Software Fault Prediction," Mathematics, MDPI, vol. 11(11), pages 1-28, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2438-:d:1155122
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    References listed on IDEAS

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    1. Mohd Nadhir Ab Wahab & Samia Nefti-Meziani & Adham Atyabi, 2015. "A Comprehensive Review of Swarm Optimization Algorithms," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-36, May.
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