Architectural technical debt: an identification strategy
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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 to 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 little 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.
Boris Pérez, Universidad Francisco de Paula Santander. Cúcuta, Colombia
https://orcid.org/0000-0001-9249-1756
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Accepted 2023-09-12
Published 2023-06-26
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