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

doi:10.1007/978-3-030-52856-0_20

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 proceeding › Conference contribution

5 Citations (Scopus)

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 language	English
Title of host publication	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Pages	257-269
Number of pages	13
ISBN (Electronic)	9783030528553
DOIs	https://doi.org/10.1007/978-3-030-52856-0_20
Publication status	Published - 1 Jan 2020
Externally published	Yes
Event	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST - Duration: 1 Jan 2020 → …

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	325 LNICST
ISSN (Print)	1867-8211

Conference

Conference	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Period	1/01/20 → …

Keywords

Defect prediction technique
Machine Learning techniques
Software Defect Prediction
Software defects

Access to Document

10.1007/978-3-030-52856-0_20

Cite this

Sohan, M. F., Kabir, M. A., Rahman, M., Bhuiyan, T., Jabiullah, M. I., & Felix, E. A. (2020). Prevalence of machine learning techniques in software defect prediction. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (pp. 257-269). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 325 LNICST). https://doi.org/10.1007/978-3-030-52856-0_20

Sohan, Md Fahimuzzman ; Kabir, Md Alamgir ; Rahman, Mostafijur et al. / Prevalence of machine learning techniques in software defect prediction. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. 2020. pp. 257-269 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{7027ccc319ab418b88987e24acd80af8,

title = "Prevalence of machine learning techniques in software defect prediction",

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.",

keywords = "Defect prediction technique, Machine Learning techniques, Software Defect Prediction, Software defects",

author = "Sohan, {Md Fahimuzzman} and Kabir, {Md Alamgir} and Mostafijur Rahman and Touhid Bhuiyan and Jabiullah, {Md Ismail} and Felix, {Ebubeogu Amarachukwu}",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-52856-0_20",

language = "English",

isbn = "9783030528553",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

pages = "257--269",

booktitle = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

note = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST ; Conference date: 01-01-2020",

}

Sohan, MF, Kabir, MA, Rahman, M, Bhuiyan, T, Jabiullah, MI & Felix, EA 2020, Prevalence of machine learning techniques in software defect prediction. in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 325 LNICST, pp. 257-269, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 1/01/20. https://doi.org/10.1007/978-3-030-52856-0_20

Prevalence of machine learning techniques in software defect prediction. / Sohan, Md Fahimuzzman; Kabir, Md Alamgir; Rahman, Mostafijur et al.
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. 2020. p. 257-269 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 325 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Prevalence of machine learning techniques in software defect prediction

AU - Sohan, Md Fahimuzzman

AU - Kabir, Md Alamgir

AU - Rahman, Mostafijur

AU - Bhuiyan, Touhid

AU - Jabiullah, Md Ismail

AU - Felix, Ebubeogu Amarachukwu

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Defect prediction technique

KW - Machine Learning techniques

KW - Software Defect Prediction

KW - Software defects

U2 - 10.1007/978-3-030-52856-0_20

DO - 10.1007/978-3-030-52856-0_20

M3 - Conference contribution

SN - 9783030528553

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 257

EP - 269

BT - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

T2 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

Y2 - 1 January 2020

ER -

Sohan MF, Kabir MA, Rahman M, Bhuiyan T, Jabiullah MI, Felix EA. Prevalence of machine learning techniques in software defect prediction. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. 2020. p. 257-269. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-52856-0_20

Prevalence of machine learning techniques in software defect prediction

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this