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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión 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 originalInglés
Título de la publicación alojadaHuman 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 alojadaFuture Systems IHIET-FS 2021
EditoresTareq Ahram, Redha Taiar
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas890-898
Número de páginas9
ISBN (versión impresa)9783030855390
DOI
EstadoPublicada - 2022
Evento5th 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. 202129 ago. 2021

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen319
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia5th 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
CiudadVirtual, Online
Período27/08/2129/08/21

Nota bibliográfica

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

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