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

Artificial Intelligence and Facial Recognition Research Group (GIIAR)

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

Abstract

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.

Original language	English
Title of host publication	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
Subtitle of host publication	Future Systems IHIET-FS 2021
Editors	Tareq Ahram, Redha Taiar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	890-898
Number of pages	9
ISBN (Print)	9783030855390
DOIs	https://doi.org/10.1007/978-3-030-85540-6_113
State	Published - 2022
Event	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 Duration: 27 Aug 2021 → 29 Aug 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	319
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	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
City	Virtual, Online
Period	27/08/21 → 29/08/21

Bibliographical note

Funding Information:
Acknowledgments. This work has been supported by the GIIAR research group and the Universidad Politécnica Salesiana.

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

Keywords

Machine learning
MCDM
NASA dataset
Software defect prediction
VIKOR method

Access to Document

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

Cite this

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. In 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.",

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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. in 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).

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

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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. In Ahram T, Taiar R, editors, 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

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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