iPanda: An LLM-based Agent for Automated Conformance Testing of Communication Protocols

• URL: http://arxiv.org/abs/2507.00378v2
• Date: Tue, 29 Jul 2025 11:56:25 GMT
• Title: iPanda: An LLM-based Agent for Automated Conformance Testing of Communication Protocols
• Authors: Xikai Sun, Fan Dang, Shiqi Jiang, Jingao Xu, Kebin Liu, Xin Miao, Zihao Yang, Weichen Zhang, Haimo Lu, Yawen Zheng, Yunhao Liu,
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive text comprehension and code generation abilities.<n>We propose iPanda, the first framework that leverages LLMs to automate protocol conformance testing.<n>Experiments on various protocols show that iPanda significantly outperforms pure LLM-based approaches.
• Score: 11.749977502129898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conformance testing is essential for ensuring that protocol implementations comply with their specifications. However, traditional testing approaches involve manually creating numerous test cases and scripts, making the process labor-intensive and inefficient. Recently, Large Language Models (LLMs) have demonstrated impressive text comprehension and code generation abilities, providing promising opportunities for automation. In this paper, we propose iPanda, the first framework that leverages LLMs to automate protocol conformance testing. Given a protocol specification document and its implementation, iPanda first employs a keyword-based method to automatically generate comprehensive test cases. Then, it utilizes retrieval-augmented generation and customized CoT strategy to effectively interpret the implementation and produce executable test programs. To further enhance programs' quality, iPanda incorporates an iterative optimization mechanism to refine generated test scripts interactively. Finally, by executing and analyzing the generated tests, iPanda systematically verifies compliance between implementations and protocol specifications. Comprehensive experiments on various protocols show that iPanda significantly outperforms pure LLM-based approaches, improving the success rate (Pass@1) of test-program generation by factors ranging from 4.675 times to 10.751 times.

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