Causal Models in Requirement Specifications for Machine Learning: A vision
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915254495084544 |
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| author | Heyn, Hans-Martin Mao, Yufei Weiss, Roland Knauss, Eric |
| author_facet | Heyn, Hans-Martin Mao, Yufei Weiss, Roland Knauss, Eric |
| contents | Specifying data requirements for machine learning (ML) software systems remains a challenge in requirements engineering (RE). This vision paper explores causal modelling as an RE activity that allows the systematic integration of prior domain knowledge into the design of ML software systems. We propose a workflow to elicit low-level model and data requirements from high-level prior knowledge using causal models. The approach is demonstrated on an industrial fault detection system. This paper outlines future research needed to establish causal modelling as an RE practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Models in Requirement Specifications for Machine Learning: A vision Heyn, Hans-Martin Mao, Yufei Weiss, Roland Knauss, Eric Software Engineering D.2.1; D.2.2 Specifying data requirements for machine learning (ML) software systems remains a challenge in requirements engineering (RE). This vision paper explores causal modelling as an RE activity that allows the systematic integration of prior domain knowledge into the design of ML software systems. We propose a workflow to elicit low-level model and data requirements from high-level prior knowledge using causal models. The approach is demonstrated on an industrial fault detection system. This paper outlines future research needed to establish causal modelling as an RE practice. |
| title | Causal Models in Requirement Specifications for Machine Learning: A vision |
| topic | Software Engineering D.2.1; D.2.2 |
| url | https://arxiv.org/abs/2502.11629 |