2023/08/11 by Sathiya Kumaran Mani, Yajie Zhou, Mani, Sathiya Kumaran +9 · 5 citations
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Software Engineering Research #Software System Performance and Reliability #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2308.06261
openalex publication_date 2023/08/11 · openalex created_date 2023/08/15 · openalex updated_date 2026/07/28
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.