dor_id: 4150091

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590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

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650.#.4.x: Ingenierías

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

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883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

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850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/1749/994

100.1.#.a: Silva, Robson Keemps; Farias, Kleinner Silva; Kunst, Rafael

524.#.#.a: Silva, Robson Keemps, et al. (2023). On the prediction of source code design problems: A systematic mapping study. Journal of Applied Research and Technology; Vol. 21 Núm. 3, 2023; 319-337. Recuperado de https://repositorio.unam.mx/contenidos/4150091

245.1.0.a: On the prediction of source code design problems: A systematic mapping study

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2023

264.#.1.c: 2023-06-27

653.#.#.a: Bad Smells; Software Engineering; Metrics; Software Analytics; Design

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

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520.3.#.a: Context Nowadays, the prediction of source code design problems plays an essential role in the software development industry, identifying defective architectural modules in advance. For this reason, some studies explored this subject in the last decade. Researchers and practitioners often need to create an overview of such studies considering the predictors of design problems, their main contributions, the used prediction techniques and research methods. Problem However, the current literature remains scarce of studies proposing a detailed mapping of studies already published. Objective This article, therefore, focuses on classifying the current literature and pinpointing trends and challenges worth investigating in this research field. Method A systematic mapping of the literature was designed and performed based on well-established practical guidelines. In total, 35 primary studies were selected, analyzed, and categorized after applying a careful filtering process from a corpus of 894 candidate studies to answer six research questions. Results The main results are that a majority of the primary studies (1) explore Bloater bad smells, (2) use code complexity and size as predictors, (3) apply machine learning techniques to generate predictions, and (4) present a prediction proposal without an extensive empirical assessment. Conclusions Predicting design problems is still in its infancy, showing that there is plenty of room for future work. Finally, this study can serve as a starting point for the research community

773.1.#.t: Journal of Applied Research and Technology; Vol. 21 Núm. 3 (2023); 319-337

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 319-337

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2023.21.3.1749

harvesting_date: 2023-11-08 13:10:00.0

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Artículo

On the prediction of source code design problems: A systematic mapping study

Silva, Robson Keemps; Farias, Kleinner Silva; Kunst, Rafael

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Silva, Robson Keemps, et al. (2023). On the prediction of source code design problems: A systematic mapping study. Journal of Applied Research and Technology; Vol. 21 Núm. 3, 2023; 319-337. Recuperado de https://repositorio.unam.mx/contenidos/4150091

Descripción del recurso

Autor(es)
Silva, Robson Keemps; Farias, Kleinner Silva; Kunst, Rafael
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
On the prediction of source code design problems: A systematic mapping study
Fecha
2023-06-27
Resumen
Context Nowadays, the prediction of source code design problems plays an essential role in the software development industry, identifying defective architectural modules in advance. For this reason, some studies explored this subject in the last decade. Researchers and practitioners often need to create an overview of such studies considering the predictors of design problems, their main contributions, the used prediction techniques and research methods. Problem However, the current literature remains scarce of studies proposing a detailed mapping of studies already published. Objective This article, therefore, focuses on classifying the current literature and pinpointing trends and challenges worth investigating in this research field. Method A systematic mapping of the literature was designed and performed based on well-established practical guidelines. In total, 35 primary studies were selected, analyzed, and categorized after applying a careful filtering process from a corpus of 894 candidate studies to answer six research questions. Results The main results are that a majority of the primary studies (1) explore Bloater bad smells, (2) use code complexity and size as predictors, (3) apply machine learning techniques to generate predictions, and (4) present a prediction proposal without an extensive empirical assessment. Conclusions Predicting design problems is still in its infancy, showing that there is plenty of room for future work. Finally, this study can serve as a starting point for the research community
Tema
Bad Smells; Software Engineering; Metrics; Software Analytics; Design
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces