Framework and Automation Levels for AI-Assisted Network Protocol Testing

Status of This Memo

This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79.¶

Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at https://datatracker.ietf.org/drafts/current/.¶

Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress."¶

This Internet-Draft will expire on 26 August 2026.¶

Copyright Notice

Copyright (c) 2026 IETF Trust and the persons identified as the document authors. All rights reserved.¶

This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document.¶

Table of Contents

1. Introduction

As protocol specifications evolve at an increasing pace, traditional testing approaches that rely heavily on manual effort or protocol-specific models struggle to keep up. Protocol testing aims to validate whether a device's behavior conforms to the semantics defined by the protocol, which are typically specified in RFC documents. In recent years, emerging application domains, including the industrial Internet, low-altitude economy, modern datacenter networks, and satellite Internet, have further accelerated the emergence of proprietary or rapidly evolving protocols. This trend significantly exacerbates the difficulty of achieving comprehensive and timely protocol testing.¶

This document proposes an automated network protocol testing framework designed to reduce manual effort, improve test coverage, and adapt efficiently to evolving specifications. The framework consists of four key modules: protocol formalization, test case generation, test script and configuration generation, and feedback-based refinement. It emphasizes modularity, reuse of existing protocol knowledge, and AI-assisted processes to enable accurate, scalable, and maintainable protocol testing.¶

In addition, this document introduces six Automation Maturity Levels (Levels 0-5) to characterize the maturity of automation in network protocol testing. These levels serve as a technology roadmap that helps researchers and practitioners assess the current capabilities of their testing systems and identify directions for future improvement. Each level captures progressively stronger capabilities in protocol formalization, orchestration, analysis, and independence from human intervention.¶

2. Definition and Acronyms

DUT: Device Under Test¶

Tester: A network device implementing multiple network protocols to support protocol conformance and performance testing. It generates test-specific packets or traffic, emulates target network behaviors, and analyzes received packets to evaluate protocol compliance and performance.¶

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.¶

Tester Script: An executable program or sequence of instructions that controls a protocol tester to generate test traffic, interact with the DUT according to a specified test case, and collect relevant observations for result evaluation.¶

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. 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. Test Topology: Each test case must specify the network topology it requires. Before executing a test case, the corresponding topology must be established accordingly. In a batch testing scenario, frequent changes in topology can be time-consuming and inefficient. To mitigate this overhead, it is common practice to construct a minimal common topology that satisfies the requirements of all test cases in a given batch. This minimizes the number of devices and links needed while ensuring that each test case can be executed within the shared topology.¶

5. 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).¶

6. Tester Configuration and 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 illustrated as follows.¶

                                                       +----------+
               +-------------+   +-----------------+   | Test Env.|
  +--------+   |  Protocol   |   |  Tester Script  |   | +------+ |
  |  RFC   |   |Formalization|   |       and       |   | |Tester| |
  |Document|-->|-------------|-->|DUT Configuration|-->| +-^----+ |
  +--------+   |  Test Case  |   |   Generation    |   |   |  |   |
               | Generation  |   +-----------------+   | +----v-+ |
               +-------------+           ^             | | 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 set of Automation Maturity Levels. These levels reflect technical progress from fully manual testing to self-optimizing, autonomous systems. These Automation Maturity Levels are intended as a reference model, not as a fixed pipeline structure.¶

As shown in Table 1, the Automation Maturity Levels are characterized along four dimensions: RFC interpretation, test asset generation and execution, result analysis, and human involvement. 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 Level 2 to Level 3)

The emergence of LLMs has significantly advanced the degree of automation achievable in network protocol testing. Within the proposed framework, LLMs can serve as core components in multiple stages of the testing pipeline, enabling a transition from Level 2 (Partial Automation) to Level 3 (Conditional Automation). A key enabler is to introduce an explicit protocol formalization step that transforms unstructured RFC text into a structured, machine-interpretable intermediate representation (e.g., a protocol description spanning message formats, state machines, and normative behavioral constraints). With such a representation, downstream generation becomes more systematic and less dependent on ad-hoc, protocol-specific parsers.¶

At the protocol formalization stage, LLMs can enrich RFCs with structured signals, such as section-level summaries, cross-references across documents, and normative requirement statements (e.g., "must" and "SHOULD"). The agent can further induce protocol modules (e.g., message formats, state machines, event-action rules, and algorithms) and formalize them into a unified representation that supports traversal and query. This representation serves as the semantic backbone for test generation, and it also helps in update scenarios by localizing changes and propagating them to the corresponding formal modules.¶

Based on the formalized protocol representation, LLMs can generate test cases in a more structured manner by decoupling test case templates from test parameters. The template generation step expands extracted test points into parameterized templates that define test objectives, topology, execution steps, oracles, and static testbed configurations. The parameter instantiation step then populates template placeholders with concrete values, including representative boundary values and invalid values for robustness testing. When oracle values require computation, the system can synthesize small helper programs (e.g., Python scripts) to compute expected outcomes, and can apply equivalence partitioning to reduce redundant parameter combinations without sacrificing meaningful coverage.¶

For test execution, LLMs can assist in translating abstract test procedures into executable artifacts for both the tester and the DUT. In practice, this translation is often a multi-step workflow that benefits from a structured agent architecture. For example, a core agent can orchestrate the artifact generation process, while specialized sub-agents handle documentation summarization, intent rewriting (turning high-level test objectives into API-aligned actions), and recurring fault fixing based on an experience pool. During execution, feedback from logs and device outputs can be used to iteratively refine generated artifacts, and an adaptive pruning mechanism can decide whether to stop exploring additional parameter instances for a given template when definitive failures are found or sufficient coverage has been achieved.¶

Despite these capabilities, it is important to note that LLMs are fundamentally probabilistic and cannot guarantee determinism or correctness. Therefore, even when the framework can complete an automated loop with reduced human effort, human oversight remains valuable for validating critical intermediate artifacts (e.g., formalized protocol modules, test oracles, and high-impact configuration changes) and for handling ambiguous or novel protocol behaviors. Nevertheless, integrating LLMs with explicit protocol formalization, systematic template/parameter generation, and execution-time feedback 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