Systematic Literature Review: Machine Learning for Software Fault Prediction

Gabriel Omar Navarro Cedeno; Katherine Cortes Moya; Ahmed Somarribas Dormond; Antonio Gonzalez-Torres; Yenory Rojas-Hernandez

doi:10.1109/CONCAPANXLI59599.2023.10517566

Systematic Literature Review: Machine Learning for Software Fault Prediction

Gabriel Omar Navarro Cedeno
, Katherine Cortes Moya
, Ahmed Somarribas Dormond
, Antonio Gonzalez-Torres
, Yenory Rojas-Hernandez

Escuela de Ingeniería en Computadores

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

1 Scopus citations

Abstract

This article presents a systematic review of the literature on the use of machine learning for software fault prediction. The objective of the paper is to determine how machine learning algorithms have been used in the approach of models for this type of prediction. The analysis carried out contemplates 52 articles that were published between 2009 and 2022. The study covers the categorization of the algorithms based on the way they were used in the applications. The results showed that the most used algorithms are based on supervised learning, Support Vector Machine (SVM), Random Forest and Naive Bayes; however, the most effective prediction models used a combination of different algorithms.

Original language	English
Title of host publication	Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350380927
DOIs	https://doi.org/10.1109/CONCAPANXLI59599.2023.10517566
State	Published - 2023
Event	41st IEEE Central America and Panama Convention, CONCAPAN 2023 - Tegucigalpa, Honduras Duration: 8 Nov 2023 → 10 Nov 2023

Publication series

Name	Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023

Conference

Conference	41st IEEE Central America and Panama Convention, CONCAPAN 2023
Country/Territory	Honduras
City	Tegucigalpa
Period	8/11/23 → 10/11/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Deep learning
algorithms
defect prediction
error prediction
fault prediction
machine learning
neural networks
software

Access to Document

10.1109/CONCAPANXLI59599.2023.10517566

Cite this

Navarro Cedeno, G. O., Moya, K. C., Dormond, A. S., Gonzalez-Torres, A., & Rojas-Hernandez, Y. (2023). Systematic Literature Review: Machine Learning for Software Fault Prediction. In Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023 (Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONCAPANXLI59599.2023.10517566

Navarro Cedeno, Gabriel Omar ; Moya, Katherine Cortes ; Dormond, Ahmed Somarribas et al. / Systematic Literature Review : Machine Learning for Software Fault Prediction. Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023).

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title = "Systematic Literature Review: Machine Learning for Software Fault Prediction",

abstract = "This article presents a systematic review of the literature on the use of machine learning for software fault prediction. The objective of the paper is to determine how machine learning algorithms have been used in the approach of models for this type of prediction. The analysis carried out contemplates 52 articles that were published between 2009 and 2022. The study covers the categorization of the algorithms based on the way they were used in the applications. The results showed that the most used algorithms are based on supervised learning, Support Vector Machine (SVM), Random Forest and Naive Bayes; however, the most effective prediction models used a combination of different algorithms.",

keywords = "Deep learning, algorithms, defect prediction, error prediction, fault prediction, machine learning, neural networks, software",

author = "\{Navarro Cedeno\}, \{Gabriel Omar\} and Moya, \{Katherine Cortes\} and Dormond, \{Ahmed Somarribas\} and Antonio Gonzalez-Torres and Yenory Rojas-Hernandez",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 41st IEEE Central America and Panama Convention, CONCAPAN 2023 ; Conference date: 08-11-2023 Through 10-11-2023",

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doi = "10.1109/CONCAPANXLI59599.2023.10517566",

language = "Ingl{\'e}s",

series = "Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023",

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Navarro Cedeno, GO, Moya, KC, Dormond, AS, Gonzalez-Torres, A & Rojas-Hernandez, Y 2023, Systematic Literature Review: Machine Learning for Software Fault Prediction. in Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023. Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023, Institute of Electrical and Electronics Engineers Inc., 41st IEEE Central America and Panama Convention, CONCAPAN 2023, Tegucigalpa, Honduras, 8/11/23. https://doi.org/10.1109/CONCAPANXLI59599.2023.10517566

Systematic Literature Review: Machine Learning for Software Fault Prediction. / Navarro Cedeno, Gabriel Omar; Moya, Katherine Cortes; Dormond, Ahmed Somarribas et al.
Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023).

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

TY - GEN

T1 - Systematic Literature Review

T2 - 41st IEEE Central America and Panama Convention, CONCAPAN 2023

AU - Navarro Cedeno, Gabriel Omar

AU - Moya, Katherine Cortes

AU - Dormond, Ahmed Somarribas

AU - Gonzalez-Torres, Antonio

AU - Rojas-Hernandez, Yenory

PY - 2023

Y1 - 2023

N2 - This article presents a systematic review of the literature on the use of machine learning for software fault prediction. The objective of the paper is to determine how machine learning algorithms have been used in the approach of models for this type of prediction. The analysis carried out contemplates 52 articles that were published between 2009 and 2022. The study covers the categorization of the algorithms based on the way they were used in the applications. The results showed that the most used algorithms are based on supervised learning, Support Vector Machine (SVM), Random Forest and Naive Bayes; however, the most effective prediction models used a combination of different algorithms.

AB - This article presents a systematic review of the literature on the use of machine learning for software fault prediction. The objective of the paper is to determine how machine learning algorithms have been used in the approach of models for this type of prediction. The analysis carried out contemplates 52 articles that were published between 2009 and 2022. The study covers the categorization of the algorithms based on the way they were used in the applications. The results showed that the most used algorithms are based on supervised learning, Support Vector Machine (SVM), Random Forest and Naive Bayes; however, the most effective prediction models used a combination of different algorithms.

KW - Deep learning

KW - algorithms

KW - defect prediction

KW - error prediction

KW - fault prediction

KW - machine learning

KW - neural networks

KW - software

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M3 - Contribución a la conferencia

AN - SCOPUS:85193260616

T3 - Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 8 November 2023 through 10 November 2023

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Navarro Cedeno GO, Moya KC, Dormond AS, Gonzalez-Torres A, Rojas-Hernandez Y. Systematic Literature Review: Machine Learning for Software Fault Prediction. In Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023). doi: 10.1109/CONCAPANXLI59599.2023.10517566

Systematic Literature Review: Machine Learning for Software Fault Prediction

Abstract

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Conference

UN SDGs

Keywords

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