Scoping of Non-Functional Requirements for Machine Learning Systems
Paper in proceeding, 2024

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].

Author

Khan Mohammad Habibullah

University of Gothenburg

Juan Garcia Diaz

University of Gothenburg

Gregory Gay

University of Gothenburg

Jennifer Horkoff

University of Gothenburg

Software Engineering 1

Proceedings of the IEEE International Conference on Requirements Engineering

1090705X (ISSN) 23326441 (eISSN)

496-497
9798350395112 (ISBN)

32nd IEEE International Requirements Engineering Conference, RE 2024
Reykjavik, Iceland,

Subject Categories

Software Engineering

Computer Science

DOI

10.1109/RE59067.2024.00061

More information

Latest update

9/9/2024 8