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

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Yining Jiang , Wei Xu , Qing Song , Yu‐Ling Lin , X. Shirley Liu , X. Y. Zheng , Qiang Su , Lizhao You , Lu Tang , Wuhu Feng , Linghe Kong , Qiao Xiang , Jiwu Shu ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2509.21074

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.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2509.21074
PDF: https://arxiv.org/pdf/2509.21074
OA Status: green
OpenAlex ID: https://openalex.org/W4414790478

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OpenAlex ID: https://openalex.org/W4414790478

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DOI: https://doi.org/10.48550/arxiv.2509.21074

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

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-09-25

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Authors: Yining Jiang, Wei Xu, Qing Song, Yu‐Ling Lin, X. Shirley Liu, X. Y. Zheng, Qiang Su, Lizhao You, Lu Tang, Wuhu Feng, Linghe Kong, Qiao Xiang, Jiwu Shu

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Landing page: https://arxiv.org/abs/2509.21074

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PDF URL: https://arxiv.org/pdf/2509.21074

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OA status: green

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Cited by: 0

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