AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results suggest that increasing \textit{smell density} was generally associated with lower test-suite-based functional correctness, although non-smelly requirements could still produce faulty code, and motivates further investigation into task-dependent quality effects in LLM-assisted software engineering.
The findings provide empirical evidence that LLM support does not necessarily improve performance and may, instead, hinder it for novice inspectors, and suggest that learning RI with LLM-support from the beginning may slow down the skill acquisition process.
This paper proposes two complementary artifacts: an Agency Justification Record (AJR) helps teams decide when an agent is warranted over simpler alternatives and an Agentic Delegation Policy (ADP) captures what must be specified for safe and effective development.
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