Framework and Automation Levels for AI-Assisted Network Protocol Testing

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Table of Contents

1. Introduction

As protocol specifications evolve rapidly, traditional testing methods that rely heavily on manual effort or static models struggle to keep pace. Testing involves validating that a device's behavior complies with the protocol's defined semantics, often documented in RFCs. In recent years, emerging application domains such as the industrial internet, low-altitude economy and satellite internet have further accelerated the proliferation of proprietary or rapidly changing protocols, making comprehensive and timely testing even more challenging.¶

This document proposes an automated network protocol testing framework that reduces manual effort, enhances test quality, and adapts to new specifications efficiently. The framework consists of four key modules: protocol understanding, test case generation, test script conversion, and feedback-based refinement. It emphasizes modular design, reuse of existing knowledge, and AI-assisted processes to facilitate accurate and scalable testing.¶

In addition to the proposed framework, this document also defines a six-level classification system (Levels 0 to 5) to characterize the evolution of automation maturity in network protocol testing. These levels serve as a technology roadmap, helping researchers evaluate the current state of their systems and set future goals. Each level captures increasing capabilities in protocol understanding, orchestration, analysis, and human independence.¶

2. Definition and Acronyms

DUT: Device Under Test¶

Tester: A network device for protocol conformance and performance testing. It can generate specific network traffic or emulate particular network devices to facilitate the execution of test cases.¶

LLM: Large Language Model¶

FSM: Finite State Machine¶

API: Application Programming Interface¶

CLI: Command Line Interface¶

Test Case: A specification of conditions and inputs to evaluate a protocol behavior.¶

Test Script: An executable program or sequence that carries out a test case on a device.¶

3. Network Protocol Testing Scenarios

Network protocol testing is required in many scenarios. This document outlines two common phases where protocol testing plays a critical role:¶

1. Device Development Phase: During the development of network equipment, vendors must ensure that their devices conform to protocol specifications. This requires the construction of a large number of test cases. Testing during this phase may involve both protocol testers and the DUT, or it may be performed solely through interconnection among DUTs.¶

2. Procurement Evaluation Phase: In the context of equipment acquisition by network operators or enterprises, candidate equipment suppliers need to demonstrate compliance with specified requirements. In this phase, third-party organizations typically perform the testing to ensure neutrality. This type of testing is usually conducted as black-box testing, requiring the use of protocol testers interconnected with the DUT. The test cases are executed while observing whether the DUT behaves in accordance with expected protocol specifications.¶

4. Key Elements of Network Protocol Testing

Network protocol testing is a complex and comprehensive process that typically involves multiple parties and various necessary components. The following entities are generally involved in protocol testing:¶

1. DUT: The DUT can be a physical network device (such as switches, routers, firewalls, etc.) or a virtual network device (such as FRRouting (FRR) routers and others).¶

2. Protocol Tester: A protocol tester is a specialized network device that usually implements a standard and comprehensive protocol stack. It can generate test traffic, collect and analyze incoming traffic, and produce test results. Protocol testers can typically be controlled via scripts, allowing automated interaction with the DUT to carry out protocol tests.¶

3. Test Cases: Protocol test cases may cover various categories, including protocol conformance tests, functional tests, and performance tests, etc. Each test case typically includes essential elements such as test topology, step-by-step procedures, and expected results. A well-defined test case also includes detailed configuration parameters.¶

4. DUT Configuration: Before executing a test case, the DUT must be initialized with specific configurations according to the test case requirements (setup). Throughout the test, the DUT configuration may undergo multiple modifications as dictated by the test scenario. Upon test completion, appropriate configurations are usually applied to restore the DUT to its initial state (teardown).¶

5. Tester Configuration and Execution Scripts: In test scenarios involving protocol testers, the tester often plays the active role by generating test traffic and orchestrating the test process. This requires the preparation of both tester-specific configurations and execution scripts. Tester scripts are typically designed in coordination with the DUT configurations to ensure proper interaction during the test.¶

5. Automated Network Protocol Test Framework

A typical network protocol test automation framework is as follows.¶

  +---------+                                        +----------+
  |   RFC   |   +----------+   +-----------------+   | Test Env.|
  |Documents|-->|          |-->|  Tester Script  |-->| +------+ |
  +---------+   |Test Case |   |   Generation    |   | |Tester| |
                |          |   | --------------- |   | +-^----+ |
  +---------+   |Generation|   |DUT Configuration|   |   |  |   |
  |  Human  |-->|          |-->|   Generation    |-->| +----v-+ |
  | Intent  |   +----------+   +-----------------+   | | DUT  | |
  +---------+         ^                 ^            | +------+ |
                      |                 |            +----------+
                +-----------------------------+           |  Test
                |   Feedback and Refinement   |<----------+ Report
                +-----------------------------+

6. Automation Maturity Levels in Network Protocol Testing

To describe the varying degrees of automation adopted in protocol testing practices, we define a six-level maturity model. These levels reflect technical progress from fully manual testing to self-optimizing, autonomous systems. The classification is intended as a reference model, not as a fixed pipeline structure.¶

As shown in Table 1, the automation progression can be characterized along four dimensions: specification interpretation, test orchestration, result analysis, and human oversight. Each level reflects an increasing degree of system autonomy and decreasing human involvement.¶

7. An Example of LLM-based Automated Network Protocol Test Framework (From L2 to L3)

The emergence of LLMs has significantly advanced the degree of automation achievable in network protocol testing. Within the proposed framework, LLMs serve as central agents in multiple stages of the pipeline, enabling a transition from Level 2 (Partial Automation) to Level 3 (Conditional Automation). By leveraging LLMs with domain-specific prompting strategies, the system is able to extract, synthesize, and refine testing artifacts with minimal human intervention.¶

At the protocol understanding stage, LLMs can ingest RFC documents and identify structured components such as protocol field definitions, message formats, and finite state machines. These outputs, often difficult to parse using traditional rule-based methods due to inconsistencies in document structure, are extracted by prompting the LLM with carefully designed templates. The resulting structured data serves as the semantic backbone for subsequent test case generation.¶

Based on the extracted protocol semantics, LLMs are also capable of generating test scenarios that aim to exercise key protocol behaviors. These scenarios are proposed in natural language and then instantiated into formal test cases through alignment with predefined schemas. The use of LLMs allows for dynamic adaptation, enabling the test generation process to generalize across different protocol families while maintaining context awareness. Although the coverage achieved is not exhaustive, the LLM is often effective at identifying common corner cases and expected failure modes, especially when guided by selected examples or constraint-based instructions.¶

For the script generation phase, LLMs assist in mapping abstract test cases to executable configurations/scripts for both the DUT/tester. Given appropriate configuration/API documentation and environment context, the model synthesizes CLI commands, configuration snippets, and tester control scripts (e.g., Python-based test harnesses or TCL scripts for commercial testbeds). This process is enhanced by retrieval-augmented generation, where relevant code examples or past case patterns are integrated into the model's prompt to increase the reliability and correctness of the output.¶

Finally, during the feedback and refinement loop, LLMs analyze execution logs and output discrepancies to identify failure points. While current models may not fully replace human diagnosis, they can summarize error types, hypothesize root causes, and suggest concrete revisions to test cases or script logic. These suggestions are then validated by experts before being applied, ensuring both efficiency and correctness.¶

Despite these capabilities, it is important to note that LLMs are fundamentally probabilistic and lack strong guarantees of determinism or correctness. Therefore, while the LLM-enabled system can execute a full test pipeline with reduced human effort, human oversight remains essential, particularly for verifying generated artifacts and handling ambiguous or novel scenarios. Nonetheless, the integration of LLMs marks a clear advance beyond traditional script-driven automation tools, and provides a practical path for elevating protocol testing practices toward Level 3 maturity.¶

8. Security Considerations

1. Execution of Unverified Generated Code: Automatically generated test scripts or configurations (e.g., CLI commands, tester control scripts) may include incorrect or harmful instructions that misconfigure devices or disrupt test environments. Mitigation: All generated artifacts should undergo validation, including syntax checking, semantic verification against protocol constraints, and dry-run execution in sandboxed environments.¶

2. AI-Assisted Component Risks: LLMs may produce incorrect or insecure outputs due to their probabilistic nature or prompt manipulation. Mitigation: Apply input sanitization, prompt hardening, and human-in-the-loop validation for critical operations.¶

9. IANA Considerations

This document has no IANA actions.¶

Acknowledgments

This work is supported by the National Key R&D Program of China.¶

Contributors

Zhen Li
Beijing Xinertel Technology Co., Ltd.
Email: lizhen_fz@xinertel.com¶

Zhanyou Li
Beijing Xinertel Technology Co., Ltd.
Email: lizy@xinertel.com¶

Authors' Addresses