A Framework for Managing Quality Requirements for Machine Learning-Based Software Systems
Paper i proceeding, 2024

Systems containing Machine Learning (ML) are becoming common, and the tasks performed by such systems must meet certain quality thresholds, e.g., desired levels of transparency, safety, and trust. Recent research has identified challenges in defining and measuring the achievement of non-functional requirements (NFRs) for ML systems. Managing NFRs is particularly challenging due to the differing nature and definitions of NFRs for ML systems including non-deterministic behavior, the need to scope over different system components (e.g., data, models, and code), and difficulty in establishing new measurements (e.g., measuring explainability). To address these challenges, we propose a framework for identifying, prioritizing, specifying, and measuring attainment of NFRs for ML systems. We present a preliminary evaluation of the framework via an interview study with practitioners. The framework captures a first step towards enabling practitioners to systematically deliver high-quality ML systems.

Machine Learning

Quality Framework

Requirements Engineering

Non-Functional Requirements

NFR Framework

Författare

Khan Mohammad Habibullah

Software Engineering 1

Göteborgs universitet

Gregory Gay

Göteborgs universitet

Software Engineering 1

Jennifer Horkoff

Göteborgs universitet

Software Engineering 1

Communications in Computer and Information Science

1865-0929 (ISSN) 18650937 (eISSN)

Vol. 2178 CCIS 3-20
9783031702440 (ISBN)

17th International Conference on the Quality of Information and Communications Technology, QUATIC 2024
Pisa, Italy,

Ämneskategorier

Programvaruteknik

Datavetenskap (datalogi)

DOI

10.1007/978-3-031-70245-7_1

Mer information

Senast uppdaterat

2024-10-02