Causal Models in Requirement Specifications for Machine Learning: A vision
Paper in proceeding, 2025
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.
AI Engineering
Data Requirements
Requirements Engineering
Causal Modelling
Author
Hans-Martin Heyn
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering
Yufei Mao
Siemens
Roland Weiß
Siemens
Eric Knauss
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering
Proceedings of the ACM SIGSOFT Symposium on the Foundations of Software Engineering
15397521 (ISSN)
1402-14059798400712760 (ISBN)
33rd ACM International Conference on the Foundations of Software Engineering, FSE Companion 2025
Trondheim, Norway,
Trondheim, Norway,
Subject Categories (SSIF 2025)
Software Engineering
Computer Sciences
DOI
10.1145/3696630.3731614