Automated Configuration Synthesis for Machine Learning Models: A Git-Based Requirement and Architecture Management System

Automated Configuration Synthesis for Machine Learning Models: A Git-Based Requirement and Architecture Management System

연구 분야: Software Development

학회: 2024 IEEE 32nd International Requirements Engineering Conference (RE)

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].