Scoping of Non-Functional Requirements for Machine Learning Systems
Paper i 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].

Författare

Khan Mohammad Habibullah

Göteborgs universitet

Juan Garcia Diaz

Göteborgs universitet

Gregory Gay

Göteborgs universitet

Jennifer Horkoff

Göteborgs universitet

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,

Ämneskategorier

Programvaruteknik

Datavetenskap (datalogi)

DOI

10.1109/RE59067.2024.00061

Mer information

Senast uppdaterat

2024-09-09