Toward Reproducing Network Research Results Using Large Language Models
Qiao Xiang Yuling Lin Mingjun Fang Bang Huang Siyong Huang Ridi Wen Franck Le Linghe Kong Jiwu Shu
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.