Toward Reproducing Network Research Results Using Large Language Models

TL;DR

This paper explores using large language models like ChatGPT to reproduce network research results, demonstrating feasibility through experiments and discussing future research directions.

Contribution

It introduces a novel approach to reproduce network research using LLMs, reducing manual effort and potential errors compared to traditional methods.

Findings

Feasibility demonstrated with ChatGPT reproducing network systems

Four students successfully reproduced different systems via prompt engineering

Discussion of lessons learned and open research questions

Abstract

Reproducing research results in the networking community is important for both academia and industry. The current best practice typically resorts to three approaches: (1) looking for publicly available prototypes; (2) contacting the authors to get a private prototype; and (3) manually implementing a prototype following the description of the publication. However, most published network research does not have public prototypes and private prototypes are hard to get. As such, most reproducing efforts are spent on manual implementation based on the publications, which is both time and labor consuming and error-prone. In this paper, we boldly propose reproducing network research results using the emerging large language models (LLMs). In particular, we first prove its feasibility with a small-scale experiment, in which four students with essential networking knowledge each reproduces a different networking system published in prominent conferences and journals by prompt engineering ChatGPT. We report the experiment's observations and lessons and discuss future open research questions of this proposal. This work raises no ethical issue.