A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913890899591168 |
|---|---|
| author | Ferreira, Renato Cordeiro |
| author_facet | Ferreira, Renato Cordeiro |
| contents | How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper brings, side-by-side, the architecture representation of two systems that can be used as case studies for creating the metrics-based architectural model: the SPIRA and the Ocean Guard MLES. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11295 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems Ferreira, Renato Cordeiro Software Engineering Artificial Intelligence Machine Learning D.2.11; D.2.8; I.2.0 How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper brings, side-by-side, the architecture representation of two systems that can be used as case studies for creating the metrics-based architectural model: the SPIRA and the Ocean Guard MLES. |
| title | A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems |
| topic | Software Engineering Artificial Intelligence Machine Learning D.2.11; D.2.8; I.2.0 |
| url | https://arxiv.org/abs/2506.11295 |