Automated Network Protocol Testing with LLM Agents

2025-10-15

AI Summary

Traditional network protocol testing relies on manual specification analysis, test case design, and implementation—resulting in low efficiency, high error rates, and poor scalability; existing model-based approaches still require extensive expert intervention for modeling. Method: This paper proposes NeTestLLM—the first multi-agent large language model (LLM) framework for automated heterogeneous protocol testing—leveraging hierarchical protocol semantic understanding, iterative test case generation, and runtime feedback-driven optimization to enable end-to-end generation of executable test artifacts directly from natural-language specifications. The framework integrates collaborative multi-LLM reasoning, task-specific workflow orchestration, and a closed-loop debugging mechanism. Contribution/Results: Evaluated on OSPF, RIP, and BGP protocols, NeTestLLM automatically generated 4,632 test cases, reproduced 41 known defects, achieved protocol coverage nearly four times higher than the national standard, and improved test artifact generation efficiency by 8.65×.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Multiagent Systems: Modeling other Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Network protocol testing is fundamental for modern network infrastructure. However, traditional network protocol testing methods are labor-intensive and error-prone, requiring manual interpretation of specifications, test case design, and translation into executable artifacts, typically demanding one person-day of effort per test case. Existing model-based approaches provide partial automation but still involve substantial manual modeling and expert intervention, leading to high costs and limited adaptability to diverse and evolving protocols. In this paper, we propose a first-of-its-kind system called NeTestLLM that takes advantage of multi-agent Large Language Models (LLMs) for end-to-end automated network protocol testing. NeTestLLM employs hierarchical protocol understanding to capture complex specifications, iterative test case generation to improve coverage, a task-specific workflow for executable artifact generation, and runtime feedback analysis for debugging and refinement. NeTestLLM has been deployed in a production environment for several months, receiving positive feedback from domain experts. In experiments, NeTestLLM generated 4,632 test cases for OSPF, RIP, and BGP, covering 41 historical FRRouting bugs compared to 11 by current national standards. The process of generating executable artifacts also improves testing efficiency by a factor of 8.65x compared to manual methods. NeTestLLM provides the first practical LLM-powered solution for automated end-to-end testing of heterogeneous network protocols.

Problem

• Automating labor-intensive network protocol testing using LLM agents
• Reducing manual modeling and expert intervention in protocol testing
• Generating executable test artifacts for diverse network protocols efficiently

Innovation

• Multi-agent LLMs automate end-to-end network testing
• Hierarchical protocol understanding captures complex specifications
• Iterative test case generation improves coverage efficiency