Automated Configuration Synthesis for Machine Learning Models: A Git-Based Requirement and Architecture Management System
Paper i proceeding, 2024

The design of complex distributed systems typically follows a hierarchical process, supported by highly specialized views for decomposing the design task. Requirements and architec-ture often evolve simultaneously, requiring an architectural framework that supports integrated and collaborative design, including non-functional requirements and quality views. The framework must ensure the traceability of design decisions in order to build safety cases. Integrating requirements into software development is vital for aligning intended functionality with implemented code. However, extracting data from semi-formal requirements and maintaining alignment poses challenges due to its ambiguity and variability making extracting consistent information challenging. Aligning these requirements with other project artifacts can also be difficult due to interpretation differences, often requiring manual effort and leading to complexity and potential inconsistencies in development [1].

machine learning

configuration

modelling

requirements

Författare

Abdullatif Alshriaf

Göteborgs universitet

Software Engineering 1

Hans-Martin Heyn

Software Engineering 1

Göteborgs universitet

Eric Knauss

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Göteborgs universitet

Proceedings of the IEEE International Conference on Requirements Engineering

1090705X (ISSN) 23326441 (eISSN)

488-491
9798350395112 (ISBN)

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

Ämneskategorier

Programvaruteknik

DOI

10.1109/RE59067.2024.00058

Mer information

Senast uppdaterat

2024-09-13