A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class

Producción Científica

Detalles Bibliográficos
Autores: Alkharabsheh, Khalid, Alawadi, Sadi, Kebande, Victor R., Crespo González Carvajal, Yania, Fernández Delgado, Manuel, Taboada González, José A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/74391
Acceso en línea:https://doi.org/10.1016/j.infsof.2021.106736
https://uvadoc.uva.es/handle/10324/74391
Access Level:acceso abierto
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spelling A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God classAlkharabsheh, KhalidAlawadi, SadiKebande, Victor R.Crespo González Carvajal, YaniaFernández Delgado, ManuelTaboada González, José A.Producción Científica*Context:* Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. *Objective:* This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. *Method:* A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. *Results:* Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher perfor- mance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. *Conclusions:* Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, such as Kappa, ROC, and MCC, have been used in the assessment of the classifier behavior.Elsevier2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1016/j.infsof.2021.106736https://uvadoc.uva.es/handle/10324/74391reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://doi.org/10.1016/j.infsof.2021.106736info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:uvadoc.uva.es:10324/743912026-06-13T12:44:47Z
dc.title.none.fl_str_mv A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class
title A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class
spellingShingle A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class
Alkharabsheh, Khalid
title_short A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class
title_full A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class
title_fullStr A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class
title_full_unstemmed A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class
title_sort A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class
dc.creator.none.fl_str_mv Alkharabsheh, Khalid
Alawadi, Sadi
Kebande, Victor R.
Crespo González Carvajal, Yania
Fernández Delgado, Manuel
Taboada González, José A.
author Alkharabsheh, Khalid
author_facet Alkharabsheh, Khalid
Alawadi, Sadi
Kebande, Victor R.
Crespo González Carvajal, Yania
Fernández Delgado, Manuel
Taboada González, José A.
author_role author
author2 Alawadi, Sadi
Kebande, Victor R.
Crespo González Carvajal, Yania
Fernández Delgado, Manuel
Taboada González, José A.
author2_role author
author
author
author
author
description Producción Científica
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.infsof.2021.106736
https://uvadoc.uva.es/handle/10324/74391
url https://doi.org/10.1016/j.infsof.2021.106736
https://uvadoc.uva.es/handle/10324/74391
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1016/j.infsof.2021.106736
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
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