From Machine Learning Documentation to Requirements: Bridging Processes with Requirements Languages
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908665531858944 |
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| author | Peng, Yi Heyn, Hans-Martin Horkoff, Jennifer |
| author_facet | Peng, Yi Heyn, Hans-Martin Horkoff, Jennifer |
| contents | In software engineering processes for machine learning (ML)-enabled systems, integrating and verifying ML components is a major challenge. A prerequisite is the specification of ML component requirements, including models and data, an area where traditional requirements engineering (RE) processes face new obstacles. An underexplored source of RE-relevant information in this context is ML documentation such as ModelCards and DataSheets. However, it is uncertain to what extent RE-relevant information can be extracted from these documents. This study first investigates the amount and nature of RE-relevant information in 20 publicly available ModelCards and DataSheets. We show that these documents contain a significant amount of potentially RE-relevant information. Next, we evaluate how effectively three established RE representations (EARS, Rupp's template, and Volere) can structure this knowledge into requirements. Our results demonstrate that there is a pathway to transform ML-specific knowledge into structured requirements, incorporating ML documentation in software engineering processes for ML systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Machine Learning Documentation to Requirements: Bridging Processes with Requirements Languages Peng, Yi Heyn, Hans-Martin Horkoff, Jennifer Software Engineering D.2.1, I.2 In software engineering processes for machine learning (ML)-enabled systems, integrating and verifying ML components is a major challenge. A prerequisite is the specification of ML component requirements, including models and data, an area where traditional requirements engineering (RE) processes face new obstacles. An underexplored source of RE-relevant information in this context is ML documentation such as ModelCards and DataSheets. However, it is uncertain to what extent RE-relevant information can be extracted from these documents. This study first investigates the amount and nature of RE-relevant information in 20 publicly available ModelCards and DataSheets. We show that these documents contain a significant amount of potentially RE-relevant information. Next, we evaluate how effectively three established RE representations (EARS, Rupp's template, and Volere) can structure this knowledge into requirements. Our results demonstrate that there is a pathway to transform ML-specific knowledge into structured requirements, incorporating ML documentation in software engineering processes for ML systems. |
| title | From Machine Learning Documentation to Requirements: Bridging Processes with Requirements Languages |
| topic | Software Engineering D.2.1, I.2 |
| url | https://arxiv.org/abs/2511.15340 |