Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura - Boris Rainiero Pérez-Gutiérrez; Darío Ernesto Correal; Fredy Humberto Vera-Rivera (Mundo Fesc)
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.