  Analyzing network topologies and communication graphs plays a crucial role in
contemporary network management. However, the absence of a cohesive approach
leads to a challenging learning curve, heightened errors, and inefficiencies.
In this paper, we introduce a novel approach to facilitate a
natural-language-based network management experience, utilizing large language
models (LLMs) to generate task-specific code from natural language queries.
This method tackles the challenges of explainability, scalability, and privacy
by allowing network operators to inspect the generated code, eliminating the
need to share network data with LLMs, and concentrating on application-specific
requests combined with general program synthesis techniques. We design and
evaluate a prototype system using benchmark applications, showcasing high
accuracy, cost-effectiveness, and the potential for further enhancements using
complementary program synthesis techniques.
