Systematic mapping of non-functional requirements and their impacts in architectures for artificial intelligence

Authors

  • Oscar Santiago Lopez Erazo Fundación Universitaria de Popayán
  • Yefry Astaiza Logiciel, Fundación Universitaria de Popayán
  • Luis Freddy Muñoz Sanabria Logiciel, Fundación Universitaria de Popayán
  • Juliana Delle Ville Lifia, Universidad Nacional de la Plata
  • Giuliana Maltempo LIFIA, Facultad de Informática, Universidad Nacional de La Plata
  • Leandro Antonelli Lifia, Universidad Nacional de la Plata
  • Julio Ariel Hurtado Alegria Idis, Universidad del Cauca
  • Cesar Collazos Idis, Universidad del Cauca
  • Pablo Ruiz Idis, Universidad del Cauca
  • Vanessa Agredo Idis, Universidad del Cauca

DOI:

https://doi.org/10.19153/cleiej.29.1.2

Keywords:

Artificial Intelligence, Non functional requirements, Software Architecture, Systematic Mapping, Impact

Abstract

Artificial intelligence (AI) is a branch of computer science that seeks algorithms capable of learning, reasoning, adapting, and performing tasks in a human-like manner. Software is determined by a set of functional requirements (FRs) and non-functional requirements (NFRs). NFRs, such as scalability, security, performance, and usability, define how a system should function beyond its basic functionalities. The architecture must be designed to meet these NFRs, ensuring system efficiency and reliability while maintaining a flexible and modular structure. Project-specific requirements, technological advancements, and the domain greatly influence the variety of NFRs and architectures that will be essential for the development of AI-based systems. Selecting an appropriate architecture is a process intrinsically guided by the prioritized NFRs. This systematic mapping seeks to provide the NFRs, architectures, and how these impact the selection of architectures for AI-based systems. The evidence gathered shows that NFRs tend to prioritize security, scalability, and accuracy. At the architectural level, there is a similar trend toward patterns such as microservices and lambda. Finally, in terms of impact, microservices is highly influenced by reliability and scalability, workflow orchestration by transparency and explainability, and federated learning by sensitive data security.

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Published

2026-03-13

Issue

Vol. 29 No. 1 (2026): Regular papers and Selected papers from the Iberoamerican Human-Computer Interaction Conference 2025

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Articles

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Copyright (c) 2026 Oscar Santiago Lopez Erazo, Yefry Astaiza, Luis Freddy Muñoz Sanabria, Juliana Delle Ville, Giuliana Maltempo, Leandro Antonelli, Julio Ariel Hurtado Alegria, Cesar Collazos, Pablo Ruiz, Vanessa Agredo

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