Languages such as P4 and NPL have enabled a wide and diverse range of networking applications that take advantage of programmable dataplanes. However, software development in these languages is difficult. To address this issue, high-level languages have been designed to offer programmers powerful abstractions that reduce the time, effort and domain-knowledge required for developing networking applications. These languages are then translated by a compiler into P4/NPL code. Inspired by the recent success of Large Language Models (LLMs) in the task of code generation, we propose to raise the level of abstraction even higher, employing LLMs to translate prose into high-level networking code. We analyze the problem, focusing on the motivation and opportunities, as well as the challenges involved and sketch out a roadmap for the development of a system that can generate high-level dataplane code from natural language instructions. We present some promising preliminary results on generating Lucid code from natural language.

翻译：诸如P4和NPL等语言已赋能众多利用可编程数据平面的多样化网络应用。然而，使用这些语言进行软件开发存在困难。为解决此问题，高级语言被设计用于为程序员提供强大的抽象机制，从而减少开发网络应用所需的时间、精力和领域知识。这些语言随后通过编译器转换为P4/NPL代码。受大型语言模型在代码生成任务中近期成功的启发，我们提出将抽象层次进一步提升，利用LLM将自然语言描述转换为高级网络代码。我们分析了该问题，重点关注其动机与机遇，以及所涉及的挑战，并勾勒出一个能够从自然语言指令生成高级数据平面代码的系统开发路线图。我们展示了从自然语言生成Lucid代码的一些初步成果，这些结果展现出良好前景。