Prevalence of machine learning techniques in software defect prediction

Md Fahimuzzman Sohan, Md Alamgir Kabir, Mostafijur Rahman, Touhid Bhuiyan, Md Ismail Jabiullah, Ebubeogu Amarachukwu Felix

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Software Defect Prediction (SDP) is a popular research area which plays an important role for software quality. It works as an indicator of whether a software module is defect-free or defective. In this study, a review has been conducted from January 2015 to August 2019 and 165 articles are selected in the area of SDP to know the prevalence of Machine Learning (ML) techniques. These articles are collected by searching in Google Scholar, and they are published in various platforms (e.g., IEEE, Springer, Elsevier). Firstly the information has been extracted from the collected particles, and then the information has been pre-processed, categorized, visualized, and finally, the results have been reported. The result shows the most frequently used data sets, classifiers, performance metrics, and techniques in SDP. This investigation will help to find the prevalence of ML techniques in SDP and give a quick view to understand the trends of ML techniques in defect prediction research.
Original languageEnglish
Title of host publicationLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Pages257-269
Number of pages13
ISBN (Electronic)9783030528553
DOIs
Publication statusPublished - 1 Jan 2020
Externally publishedYes
EventLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST -
Duration: 1 Jan 2020 → …

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume325 LNICST
ISSN (Print)1867-8211

Conference

ConferenceLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Period1/01/20 → …

Keywords

  • Defect prediction technique
  • Machine Learning techniques
  • Software Defect Prediction
  • Software defects

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