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| Format: | Artículo científico |
| Language: | en |
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Universidad del Valle
2023
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| Online Access: | https://www.redalyc.org/articulo.oa?id=291377795015 https://www.redalyc.org/journal/2913/291377795015/ https://www.redalyc.org/journal/2913/291377795015/html/ https://www.redalyc.org/journal/2913/291377795015/291377795015.epub https://www.redalyc.org/journal/2913/291377795015/movil https://doi.org/10.25100/iyc.v25i3.13171 |
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| _version_ | 1866814764892553216 |
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| author | Boris R. Pérez |
| author_facet | Boris R. Pérez |
| contents | Architectural technical debt: an identification strategy Boris R. Pérez Ingeniería Machine learning Software architecture Identification strategy Architectural technical debt Architectural Technical Debt is a metaphor for actions made by architects to achieve short-term goals while potentially harming the system’s long-term health. Architectural Technical Debt is difficult to detect since it is associated with a system’s long-term maintenance and evolution. In this research, we describe an architectural evolution-based method for debt identification that is backed by a supervised machine learning model and is based on information obtained from artifacts produced during architecture design. We discovered that even with a small amount of data, the machine learning model produces good results in terms of Recall and even Accuracy. The trial provides insights that allow us to conclude that this idea works well and might be utilized as a starting point to assist architects in identifying and managing Architectural Technical Debt. 2023 artículo científico 0123-3033 https://www.redalyc.org/articulo.oa?id=291377795015 https://www.redalyc.org/journal/2913/291377795015/ https://www.redalyc.org/journal/2913/291377795015/html/ https://www.redalyc.org/journal/2913/291377795015/291377795015.epub https://www.redalyc.org/journal/2913/291377795015/movil https://doi.org/10.25100/iyc.v25i3.13171 en http://www.redalyc.org/revista.oa?id=2913 Ingeniería y Competitividad application/pdf Universidad del Valle Ingeniería y Competitividad (Colombia) Num.3 Vol.25 |
| format | Artículo científico |
| id | redalyc_291377795015 |
| language | en |
| publishDate | 2023 |
| publisher | Universidad del Valle |
| spellingShingle | Architectural technical debt: an identification strategy Boris R. Pérez Ingeniería Machine learning Software architecture Identification strategy Architectural technical debt Architectural technical debt: an identification strategy Boris R. Pérez Ingeniería Machine learning Software architecture Identification strategy Architectural technical debt Architectural Technical Debt is a metaphor for actions made by architects to achieve short-term goals while potentially harming the system’s long-term health. Architectural Technical Debt is difficult to detect since it is associated with a system’s long-term maintenance and evolution. In this research, we describe an architectural evolution-based method for debt identification that is backed by a supervised machine learning model and is based on information obtained from artifacts produced during architecture design. We discovered that even with a small amount of data, the machine learning model produces good results in terms of Recall and even Accuracy. The trial provides insights that allow us to conclude that this idea works well and might be utilized as a starting point to assist architects in identifying and managing Architectural Technical Debt. 2023 artículo científico 0123-3033 https://www.redalyc.org/articulo.oa?id=291377795015 https://www.redalyc.org/journal/2913/291377795015/ https://www.redalyc.org/journal/2913/291377795015/html/ https://www.redalyc.org/journal/2913/291377795015/291377795015.epub https://www.redalyc.org/journal/2913/291377795015/movil https://doi.org/10.25100/iyc.v25i3.13171 en http://www.redalyc.org/revista.oa?id=2913 Ingeniería y Competitividad application/pdf Universidad del Valle Ingeniería y Competitividad (Colombia) Num.3 Vol.25 |
| title | Architectural technical debt: an identification strategy |
| topic | Ingeniería Machine learning Software architecture Identification strategy Architectural technical debt |
| url | https://www.redalyc.org/articulo.oa?id=291377795015 https://www.redalyc.org/journal/2913/291377795015/ https://www.redalyc.org/journal/2913/291377795015/html/ https://www.redalyc.org/journal/2913/291377795015/291377795015.epub https://www.redalyc.org/journal/2913/291377795015/movil https://doi.org/10.25100/iyc.v25i3.13171 |