GraphCue for SDN Configuration Code Synthesis

TL;DR

GraphCue is a novel framework that uses topology-aware retrieval, structured prompts, and verification to automate SDN configuration code synthesis with high accuracy and efficiency.

Contribution

It introduces a topology-grounded retrieval and agent-in-the-loop framework for SDN configuration synthesis, leveraging graph embeddings and structured prompts for improved performance.

Findings

Achieves 88.2% pass rate within 20 iterations on validation cases.

Completes 95% of verification loops within 9 seconds.

Retrieval and structured prompting are crucial for performance.

Abstract

We present GraphCue, a topology-grounded retrieval and agent-in-the-loop framework for automated SDN configuration. Each case is abstracted into a JSON graph and embedded using a lightweight three-layer GCN trained with contrastive learning. The nearest validated reference is injected into a structured prompt that constrains code generation, while a verifier closes the loop by executing the candidate configuration and feeding failures back to the agent. On 628 validation cases, GraphCue achieves an 88.2 percent pass rate within 20 iterations and completes 95 percent of verification loops within 9 seconds. Ablation studies without retrieval or structured prompting perform substantially worse, indicating that topology-aware retrieval and constraint-based conditioning are key drivers of performance.