Open Access

REVIEW

Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

Faseeha Matloob¹, Shabib Aftab^1,2, Munir Ahmad², Muhammad Adnan Khan^3,*, Areej Fatima⁴, Muhammad Iqbal², Wesam Mohsen Alruwaili⁵, Nouh Sabri Elmitwally^5,6

1 Department of Computer Science, Virtual University of Pakistan, Lahore, 54000, Pakistan
2 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
3 Riphah School of Computing and Innovation, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
4 Department of Computer Science, Lahore Garrison university, Lahore, 54000, Pakistan
5 College of Computer and Information Sciences, Jouf University, Sakaka, 72341, Saudi Arabia
6 Department of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, 12613, Egypt

* Corresponding Author: Muhammad Adnan Khan. Email:

Intelligent Automation & Soft Computing 2021, 29(2), 403-421. https://doi.org/10.32604/iasc.2021.017562

Received 03 February 2021; Accepted 07 April 2021; Issue published 16 June 2021

Abstract

Software defect prediction (SDP) is the process of detecting defect-prone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are association, estimation, clustering, classification, and dataset analysis. This study provides a systematic literature review that highlights the latest research trends in the area of SDP by providing a critical review of papers published between 2016 and 2019. Initially, 1012 papers were shortlisted from three online libraries (IEEE Xplore, ACM, and ScienceDirect); following a systematic research protocol, 22 of these papers were selected for detailed critical review. This review will serve researchers by providing the most current picture of the published work on software defect classification.

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