Causal models for specifying requirements in industrial ML-based software: A case study

• Based on the results of a series of workshops with industrial practitioners, this study proposes the use of causal models as a supplement to natural language requirements for specifying software with ML components. • The paper provides a demonstration of a proposed causality-driven development concept on an industrial use case on anomaly detection in power systems. • The paper reports on initial results from laboratory experiments that indicate positive effects of the use of causal models during software development on the performance and robustness of a trained ML model for anomaly detection in an industrial prototyping setting. Unlike conventional software systems, where rules are explicitly defined to specify the desired behaviour, software components that incorporate machine learning (ML) infer such rules as associations from data. Requirements Engineering (RE) provides methods and tools for specifying the desired behaviour as structured natural language. However, the inherent ambiguity of natural language can make these specifications difficult to interpret. Moreover, it is challenging in RE to establish a clear link between the specified desired behaviour and data requirements necessary for training and validating ML models. In this paper, we explore the use of causal models to address this gap in RE. Through an exploratory case study, we found that causal models, represented as directed acyclic graphs (DAGs), support the collaborative discovery of an ML system’s operational context from a causal perspective. We also found that causal models can serve as part of the requirements specification for ML models because they encapsulate both data and model requirements needed to achieve the desired causal behaviour. We introduce a concept for causality-driven development , in which we show that data and model requirements, as well as a causal description of the operational context, can be discovered iteratively using graphical causal models. We demonstrate this approach using an industrial use case on anomaly detection with ML.