New paper outlines architecture for AI-native software development assurance

A new paper proposes an architecture for achieving "assurance closure" in AI-native large-scale agile software development. This concept means systems can establish what needs to be true, gather and judge evidence, maintain its validity through changes, and use uncertainty to limit agent authority. The authors identify six gaps in current methods and suggest a high-level architecture with six capabilities built on a shared semantic assurance layer to address these challenges. AI

IMPACT Proposes a framework to enhance dependability and human oversight in AI-driven software engineering processes.

RANK_REASON The cluster contains a single academic paper discussing a novel architecture for AI-native software development. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• AI-Native Manifesto
• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• Influence Flower
• ScienceCast

• paper
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →