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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9798350380927
DOI	https://doi.org/10.1109/CONCAPANXLI59599.2023.10517566
Estado	Publicada - 2023
Evento	41st IEEE Central America and Panama Convention, CONCAPAN 2023 - Tegucigalpa, Honduras Duración: 8 nov 2023 → 10 nov 2023

Serie de la publicación

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

Conferencia

Conferencia	41st IEEE Central America and Panama Convention, CONCAPAN 2023
País/Territorio	Honduras
Ciudad	Tegucigalpa
Período	8/11/23 → 10/11/23

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10.1109/CONCAPANXLI59599.2023.10517566

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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. En 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.",

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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",

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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. En 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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 - algorithms

KW - Deep learning

KW - defect prediction

KW - error prediction

KW - fault prediction

KW - machine learning

KW - neural networks

KW - software

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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. En 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

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