A conceptual framework reorganizes requirements engineering for pretrained-model-enabled systems into six activities, based on identified challenges of opaque capabilities, context sensitivity, and continuous evolution.
Causal Models in Requirement Specifications for Machine Learning: A vision
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abstract
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.
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A Conceptual Framework for Requirements Engineering of Pretrained-Model-Enabled Systems
A conceptual framework reorganizes requirements engineering for pretrained-model-enabled systems into six activities, based on identified challenges of opaque capabilities, context sensitivity, and continuous evolution.