A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR

Miguel Ángel Quiroz Martinez; Byron Alcívar  Martínez Tayupanda; Sulay Stephanie  Camatón Paguay; Luis Andy  Briones Peñafiel

doi:10.1007/978-3-030-85540-6_113

A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR

Miguel Ángel Quiroz Martinez, Byron Alcívar Martínez Tayupanda, Sulay Stephanie Camatón Paguay, Luis Andy Briones Peñafiel

Grupo de Investigación en Inteligencia Artificial y Reconocimiento Facial (GIIAR)

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

Resumen

In today’s time, software quality assurance is the most essential and costly set of activities during software development in the information technology (IT) industries. Finding defects in system modules has always been one of the most relevant problems in software engineering, leading to increased costs and reduced confidence in the product, resulting in dissatisfaction with customer requirements. Therefore, to provide and deliver an efficient software product with as few defects as possible on time and of good quality, it is necessary to use machine learning techniques and models, such as supervised learning to accurately classify and predict defects in each of the software development life cycle (SDLC) phases before delivering a software product to the customer. The main objective is to evaluate the performance of different machine learning models in software defect prediction applied to 4 NASA datasets, such as CM1, JM1, KC1, and PC1, then de-terminate and select the best performing model using the MCDM: VIKOR multi-criteria decision-making method.

Idioma original	Inglés
Título de la publicación alojada	Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET
Subtítulo de la publicación alojada	Future Systems IHIET-FS 2021
Editores	Tareq Ahram, Redha Taiar
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	890-898
Número de páginas	9
ISBN (versión impresa)	9783030855390
DOI	https://doi.org/10.1007/978-3-030-85540-6_113
Estado	Publicada - 2022
Evento	5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021 - Virtual, Online Duración: 27 ago. 2021 → 29 ago. 2021

Serie de la publicación

Nombre	Lecture Notes in Networks and Systems
Volumen	319

Conferencia

Conferencia	5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021
Ciudad	Virtual, Online
Período	27/08/21 → 29/08/21

Nota bibliográfica

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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10.1007/978-3-030-85540-6_113

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Martinez, M. Á. Q., Martínez Tayupanda, B. A., Camatón Paguay, S. S., & Briones Peñafiel, L. A. (2022). A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR. En T. Ahram, & R. Taiar (Eds.), Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021 (pp. 890-898). (Lecture Notes in Networks and Systems; Vol. 319). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85540-6_113

Martinez, Miguel Ángel Quiroz ; Martínez Tayupanda, Byron Alcívar ; Camatón Paguay, Sulay Stephanie et al. / A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR. Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021. editor / Tareq Ahram ; Redha Taiar. Springer Science and Business Media Deutschland GmbH, 2022. pp. 890-898 (Lecture Notes in Networks and Systems).

@inproceedings{153f9cc8dd154057929e2be0b1976d6b,

title = "A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR",

abstract = "In today{\textquoteright}s time, software quality assurance is the most essential and costly set of activities during software development in the information technology (IT) industries. Finding defects in system modules has always been one of the most relevant problems in software engineering, leading to increased costs and reduced confidence in the product, resulting in dissatisfaction with customer requirements. Therefore, to provide and deliver an efficient software product with as few defects as possible on time and of good quality, it is necessary to use machine learning techniques and models, such as supervised learning to accurately classify and predict defects in each of the software development life cycle (SDLC) phases before delivering a software product to the customer. The main objective is to evaluate the performance of different machine learning models in software defect prediction applied to 4 NASA datasets, such as CM1, JM1, KC1, and PC1, then de-terminate and select the best performing model using the MCDM: VIKOR multi-criteria decision-making method.",

keywords = "Machine learning, MCDM, NASA dataset, Software defect prediction, VIKOR method",

author = "Martinez, {Miguel {\'A}ngel Quiroz} and {Mart{\'i}nez Tayupanda}, {Byron Alc{\'i}var} and {Camat{\'o}n Paguay}, {Sulay Stephanie} and {Briones Pe{\~n}afiel}, {Luis Andy}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021 ; Conference date: 27-08-2021 Through 29-08-2021",

year = "2022",

doi = "10.1007/978-3-030-85540-6_113",

language = "English",

isbn = "9783030855390",

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publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "890--898",

editor = "Tareq Ahram and Redha Taiar",

booktitle = "Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET",

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Martinez, MÁQ, Martínez Tayupanda, BA, Camatón Paguay, SS & Briones Peñafiel, LA 2022, A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR. En T Ahram & R Taiar (eds.), Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021. Lecture Notes in Networks and Systems, vol. 319, Springer Science and Business Media Deutschland GmbH, pp. 890-898, 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021, Virtual, Online, 27/08/21. https://doi.org/10.1007/978-3-030-85540-6_113

A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR. / Martinez, Miguel Ángel Quiroz; Martínez Tayupanda, Byron Alcívar ; Camatón Paguay, Sulay Stephanie et al.
Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021. ed. / Tareq Ahram; Redha Taiar. Springer Science and Business Media Deutschland GmbH, 2022. p. 890-898 (Lecture Notes in Networks and Systems; Vol. 319).

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

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T1 - A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR

AU - Martinez, Miguel Ángel Quiroz

AU - Martínez Tayupanda, Byron Alcívar

AU - Camatón Paguay, Sulay Stephanie

AU - Briones Peñafiel, Luis Andy

PY - 2022

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N2 - In today’s time, software quality assurance is the most essential and costly set of activities during software development in the information technology (IT) industries. Finding defects in system modules has always been one of the most relevant problems in software engineering, leading to increased costs and reduced confidence in the product, resulting in dissatisfaction with customer requirements. Therefore, to provide and deliver an efficient software product with as few defects as possible on time and of good quality, it is necessary to use machine learning techniques and models, such as supervised learning to accurately classify and predict defects in each of the software development life cycle (SDLC) phases before delivering a software product to the customer. The main objective is to evaluate the performance of different machine learning models in software defect prediction applied to 4 NASA datasets, such as CM1, JM1, KC1, and PC1, then de-terminate and select the best performing model using the MCDM: VIKOR multi-criteria decision-making method.

AB - In today’s time, software quality assurance is the most essential and costly set of activities during software development in the information technology (IT) industries. Finding defects in system modules has always been one of the most relevant problems in software engineering, leading to increased costs and reduced confidence in the product, resulting in dissatisfaction with customer requirements. Therefore, to provide and deliver an efficient software product with as few defects as possible on time and of good quality, it is necessary to use machine learning techniques and models, such as supervised learning to accurately classify and predict defects in each of the software development life cycle (SDLC) phases before delivering a software product to the customer. The main objective is to evaluate the performance of different machine learning models in software defect prediction applied to 4 NASA datasets, such as CM1, JM1, KC1, and PC1, then de-terminate and select the best performing model using the MCDM: VIKOR multi-criteria decision-making method.

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Martinez MÁQ, Martínez Tayupanda BA, Camatón Paguay SS, Briones Peñafiel LA. A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR. En Ahram T, Taiar R, editores, Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET: Future Systems IHIET-FS 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 890-898. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85540-6_113

A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR

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