From Machine Learning Documentation to Requirements: Bridging Processes with Requirements Languages

• URL: http://arxiv.org/abs/2511.15340v1
• Date: Wed, 19 Nov 2025 10:59:28 GMT
• Title: From Machine Learning Documentation to Requirements: Bridging Processes with Requirements Languages
• Authors: Yi Peng, Hans-Martin Heyn, Jennifer Horkoff,
• Abstract summary: This study first investigates the amount and nature of RE-relevant information in 20 publicly available ModelCards and DataSheets.<n>Next, we evaluate how effectively three established RE representations (EARS, Rupp's template, and Volere) can structure this knowledge into requirements.
• Score: 2.2012529546432633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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