Quality Model for Machine Learning Components
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911422268571648 |
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| author | Lewis, Grace A. Brower-Sinning, Rachel Edman, Robert Ozkaya, Ipek Echeverría, Sebastián Derr, Alex Beaudoin, Collin Maffey, Katherine R. |
| author_facet | Lewis, Grace A. Brower-Sinning, Rachel Edman, Robert Ozkaya, Ipek Echeverría, Sebastián Derr, Alex Beaudoin, Collin Maffey, Katherine R. |
| contents | Despite increased adoption and advances in machine learning (ML), there are studies showing that many ML prototypes do not reach the production stage and that testing is still largely limited to testing model properties, such as model performance, without considering requirements derived from the system it will be a part of, such as throughput, resource consumption, or robustness. This limited view of testing leads to failures in model integration, deployment, and operations. In traditional software development, quality models such as ISO 25010 provide a widely used structured framework to assess software quality, define quality requirements, and provide a common language for communication with stakeholders. A newer standard, ISO 25059, defines a more specific quality model for AI systems. However, a problem with this standard is that it combines system attributes with ML component attributes, which is not helpful for a model developer, as many system attributes cannot be assessed at the component level. In this paper, we present a quality model for ML components that serves as a guide for requirements elicitation and negotiation and provides a common vocabulary for ML component developers and system stakeholders to agree on and define system-derived requirements and focus their testing efforts accordingly. The quality model was validated through a survey in which the participants agreed with its relevance and value. The quality model has been successfully integrated into an open-source tool for ML component testing and evaluation demonstrating its practical application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05043 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quality Model for Machine Learning Components Lewis, Grace A. Brower-Sinning, Rachel Edman, Robert Ozkaya, Ipek Echeverría, Sebastián Derr, Alex Beaudoin, Collin Maffey, Katherine R. Software Engineering Artificial Intelligence Despite increased adoption and advances in machine learning (ML), there are studies showing that many ML prototypes do not reach the production stage and that testing is still largely limited to testing model properties, such as model performance, without considering requirements derived from the system it will be a part of, such as throughput, resource consumption, or robustness. This limited view of testing leads to failures in model integration, deployment, and operations. In traditional software development, quality models such as ISO 25010 provide a widely used structured framework to assess software quality, define quality requirements, and provide a common language for communication with stakeholders. A newer standard, ISO 25059, defines a more specific quality model for AI systems. However, a problem with this standard is that it combines system attributes with ML component attributes, which is not helpful for a model developer, as many system attributes cannot be assessed at the component level. In this paper, we present a quality model for ML components that serves as a guide for requirements elicitation and negotiation and provides a common vocabulary for ML component developers and system stakeholders to agree on and define system-derived requirements and focus their testing efforts accordingly. The quality model was validated through a survey in which the participants agreed with its relevance and value. The quality model has been successfully integrated into an open-source tool for ML component testing and evaluation demonstrating its practical application. |
| title | Quality Model for Machine Learning Components |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2602.05043 |