From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912474518781952 |
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| author | Arora, Chetan Wang, Fanyu Tantithamthavorn, Chakkrit Aleti, Aldeida Kenyon, Shaun |
| author_facet | Arora, Chetan Wang, Fanyu Tantithamthavorn, Chakkrit Aleti, Aldeida Kenyon, Shaun |
| contents | Requirements engineering (RE) in the space industry is inherently complex, demanding high precision, alignment with rigorous standards, and adaptability to mission-specific constraints. Smaller space organisations and new entrants often struggle to derive actionable requirements from extensive, unstructured documents such as mission briefs, interface specifications, and regulatory standards. In this innovation opportunity paper, we explore the potential of Retrieval-Augmented Generation (RAG) models to support and (semi-)automate requirements generation in the space domain. We present a modular, AI-driven approach that preprocesses raw space mission documents, classifies them into semantically meaningful categories, retrieves contextually relevant content from domain standards, and synthesises draft requirements using large language models (LLMs). We apply the approach to a real-world mission document from the space domain to demonstrate feasibility and assess early outcomes in collaboration with our industry partner, Starbound Space Solutions. Our preliminary results indicate that the approach can reduce manual effort, improve coverage of relevant requirements, and support lightweight compliance alignment. We outline a roadmap toward broader integration of AI in RE workflows, intending to lower barriers for smaller organisations to participate in large-scale, safety-critical missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07689 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry Arora, Chetan Wang, Fanyu Tantithamthavorn, Chakkrit Aleti, Aldeida Kenyon, Shaun Software Engineering Requirements engineering (RE) in the space industry is inherently complex, demanding high precision, alignment with rigorous standards, and adaptability to mission-specific constraints. Smaller space organisations and new entrants often struggle to derive actionable requirements from extensive, unstructured documents such as mission briefs, interface specifications, and regulatory standards. In this innovation opportunity paper, we explore the potential of Retrieval-Augmented Generation (RAG) models to support and (semi-)automate requirements generation in the space domain. We present a modular, AI-driven approach that preprocesses raw space mission documents, classifies them into semantically meaningful categories, retrieves contextually relevant content from domain standards, and synthesises draft requirements using large language models (LLMs). We apply the approach to a real-world mission document from the space domain to demonstrate feasibility and assess early outcomes in collaboration with our industry partner, Starbound Space Solutions. Our preliminary results indicate that the approach can reduce manual effort, improve coverage of relevant requirements, and support lightweight compliance alignment. We outline a roadmap toward broader integration of AI in RE workflows, intending to lower barriers for smaller organisations to participate in large-scale, safety-critical missions. |
| title | From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2507.07689 |