RePro: Leveraging Large Language Models for Semi-Automated Reproduction of Networking Research Results

Reproducing networking research is a critical but challenging task due to the scarcity of open-source code. While Large Language Models (LLMs) can automate code generation, current approaches lack the generalizability required for the diverse networking field. To address this, we propose RePro, a semi-automated reproduction framework that leverages advanced prompt engineering to reproduce network systems from their research papers. RePro combines few-shot in-context learning with Structured and Semantic Chain of Thought (SCoT/SeCoT) techniques to systematically translate a paper's description into an optimized, executable implementation. The framework operates through a three-stage pipeline: system description extraction, structural code generation, and code optimization. Our evaluation with five state-of-the-art LLMs across diverse network sub-domains demonstrates that RePro significantly reduces reproduction time compared to manual efforts while achieving comparable system performance, validating its effectiveness and efficiency.