A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915440941334528 |
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| 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 showcases the first step for creating the metrics-based architectural model: an extension of a reference architecture that can describe MLES to collect their metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Metrics-Oriented Architectural Model to Characterize 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 showcases the first step for creating the metrics-based architectural model: an extension of a reference architecture that can describe MLES to collect their metrics. |
| title | A Metrics-Oriented Architectural Model to Characterize 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/2506.08153 |