Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura
Background: The decisions made by architects to favor short-term objectives and potentially to the detriment of long-term software quality are known as Technical Debt in Architecture. This type of technical debt is difficult to identify because it is related to quality attributes that are not visibl...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Fundación de Estudios Superiores Comfanorte
2022-01-01
|
| Series: | Mundo Fesc |
| Subjects: | |
| Online Access: | https://www.fesc.edu.co/Revistas/OJS/index.php/mundofesc/article/view/1198 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850031721445064704 |
|---|---|
| author | Boris Rainiero Pérez-Gutiérrez Darío Ernesto Correal Fredy Humberto Vera-Rivera |
| author_facet | Boris Rainiero Pérez-Gutiérrez Darío Ernesto Correal Fredy Humberto Vera-Rivera |
| author_sort | Boris Rainiero Pérez-Gutiérrez |
| collection | DOAJ |
| description | Background: The decisions made by architects to favor short-term objectives and potentially to the detriment of long-term software quality are known as Technical Debt in Architecture. This type of technical debt is difficult to identify because it is related to quality attributes that are not visible to the client, such as maintainability and the system's ability to evolve. Objective: Therefore, in this article, a supervised machine learning model is used to support the identification of technical debt in architecture, located at the architectural design stage. Methods: This proposal relies on information collected from artifacts produced during the architectural design process to build a dataset that allows the evaluation of different supervised learning algorithms and thus establish the one with the best accuracy. The identification of technical debt, within the framework of this proposal and unlike those proposed in the literature, does not consider the source code. Results: The performance of the model was evaluated through a real-life industrial case study, revealing that both accuracy and recall were at acceptable levels. Conclusions: The data used to train the model, while appropriate, is susceptible to further improvement. This approach will allow architects to support the identification of conscious and unconscious ATDs injected into their architectures. |
| format | Article |
| id | doaj-art-ce94bc1dea9c47e7acc689eb785833c6 |
| institution | DOAJ |
| issn | 2216-0353 2216-0388 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Fundación de Estudios Superiores Comfanorte |
| record_format | Article |
| series | Mundo Fesc |
| spelling | doaj-art-ce94bc1dea9c47e7acc689eb785833c62025-08-20T02:58:54ZengFundación de Estudios Superiores ComfanorteMundo Fesc2216-03532216-03882022-01-01122314415710.61799/2216-0388.1198Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitecturaBoris Rainiero Pérez-Gutiérrez0Darío Ernesto Correal1Fredy Humberto Vera-Rivera2Universidad Francisco de Paula SantanderUniversidad de los AndesUniversidad Francisco de Paula SantanderBackground: The decisions made by architects to favor short-term objectives and potentially to the detriment of long-term software quality are known as Technical Debt in Architecture. This type of technical debt is difficult to identify because it is related to quality attributes that are not visible to the client, such as maintainability and the system's ability to evolve. Objective: Therefore, in this article, a supervised machine learning model is used to support the identification of technical debt in architecture, located at the architectural design stage. Methods: This proposal relies on information collected from artifacts produced during the architectural design process to build a dataset that allows the evaluation of different supervised learning algorithms and thus establish the one with the best accuracy. The identification of technical debt, within the framework of this proposal and unlike those proposed in the literature, does not consider the source code. Results: The performance of the model was evaluated through a real-life industrial case study, revealing that both accuracy and recall were at acceptable levels. Conclusions: The data used to train the model, while appropriate, is susceptible to further improvement. This approach will allow architects to support the identification of conscious and unconscious ATDs injected into their architectures.https://www.fesc.edu.co/Revistas/OJS/index.php/mundofesc/article/view/1198arquitectura de soluciónartefactos heterogéneosdeuda técnica en arquitecturamachine learning |
| spellingShingle | Boris Rainiero Pérez-Gutiérrez Darío Ernesto Correal Fredy Humberto Vera-Rivera Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura Mundo Fesc arquitectura de solución artefactos heterogéneos deuda técnica en arquitectura machine learning |
| title | Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura |
| title_full | Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura |
| title_fullStr | Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura |
| title_full_unstemmed | Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura |
| title_short | Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura |
| title_sort | un enfoque de machine learning para apoyar la identificacion de la deuda tecnica en arquitectura |
| topic | arquitectura de solución artefactos heterogéneos deuda técnica en arquitectura machine learning |
| url | https://www.fesc.edu.co/Revistas/OJS/index.php/mundofesc/article/view/1198 |
| work_keys_str_mv | AT borisrainieroperezgutierrez unenfoquedemachinelearningparaapoyarlaidentificaciondeladeudatecnicaenarquitectura AT darioernestocorreal unenfoquedemachinelearningparaapoyarlaidentificaciondeladeudatecnicaenarquitectura AT fredyhumbertoverarivera unenfoquedemachinelearningparaapoyarlaidentificaciondeladeudatecnicaenarquitectura |