Scoping of Non-Functional Requirements for Machine Learning Systems

Machine Learning (ML) systems increasingly perform complex decision-making and prediction tasks—e.g., in autonomous driving—based on patterns inferred from large quantities of data. The inclusion of ML increases the capabilities of software systems, but also introduces or exacerbates challenges. ML systems can be more complex, time-consuming and expensive to specify, develop, and test than traditional systems, and can suffer from issues related to safety, lack of explainability, limited maintainability, and bias [1], [2]. As in other domains, ML systems must satisfy certain quality requirements—known as non-functional requirements (NFRs)—to be considered fit for purpose [1].