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

TL;DR

iPanda leverages large language models to automate communication protocol conformance testing, significantly improving test program success rates through automated test case generation, interpretation, and iterative refinement.

Contribution

This paper introduces iPanda, the first framework that uses LLMs for automated protocol conformance testing, combining test case generation, implementation interpretation, and iterative optimization.

Findings

Outperforms pure LLM approaches in success rate (Pass@1) by 4.675 to 10.751 times.

Automates comprehensive test case generation from protocol specifications.

Effectively verifies implementation compliance with protocol standards.

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.