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

2025-11-19

AI Summary

Machine learning (ML) system integration is hindered by the lack of standardized requirements specifications for ML components, rendering conventional requirements engineering (RE) practices ill-suited. Method: This study conducts the first systematic investigation into RE-relevant information embedded in ML documentation—specifically Model Cards and Data Sheets—using qualitative content analysis to code 20 publicly available documents and evaluating their mapping feasibility onto established RE frameworks (EARS, Rupp, Volere). Results: Findings reveal that current ML documentation contains rich, structurable functional and non-functional requirements—including performance constraints, operational contexts, and bias disclosures—demonstrating direct potential to support system-level requirements modeling. The paper empirically validates ML documentation as a novel, credible RE information source and establishes a principled translation pathway from unstructured ML artifacts to formal software requirements, thereby bridging the RE gap between ML engineering and traditional software engineering.

Technology Category

• Machine Learning: Scalability of ML Systems
• Knowledge Representation and Reasoning: Description Logics
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Integrating and verifying ML components in software engineering processes
• Extracting requirements-relevant information from ML documentation like ModelCards
• Transforming ML-specific knowledge into structured requirements using RE representations

Innovation

• Extracting requirements from ML documentation
• Structuring knowledge using RE representations
• Transforming ML-specific knowledge into requirements