Infrastructure as Code (IaC) is a revolutionary approach which has gained
significant prominence in the Industry. IaC manages and provisions IT
infrastructure using machine-readable code by enabling automation, consistency
across the environments, reproducibility, version control, error reduction and
enhancement in scalability. However, IaC orchestration is often a painstaking
effort which requires specialised skills as well as a lot of manual effort.
Automation of IaC is a necessity in the present conditions of the Industry and
in this survey, we study the feasibility of applying Large Language Models
(LLM) to address this problem. LLMs are large neural network-based models which
have demonstrated significant language processing abilities and shown to be
capable of following a range of instructions within a broad scope. Recently,
they have also been adapted for code understanding and generation tasks
successfully, which makes them a promising choice for the automatic generation
of IaC configurations. In this survey, we delve into the details of IaC, usage
of IaC in different platforms, their challenges, LLMs in terms of
code-generation aspects and the importance of LLMs in IaC along with our own
experiments. Finally, we conclude by presenting the challenges in this area and
highlighting the scope for future research.

基础设施即代码（IaC）是一种在产业中获得显著重视的革命性方法，该方法通过使用机器可读代码来管理和提供 IT 基础设施，实现自动化、环境一致性、可重复性、版本控制、错误减少和可伸缩性提升。本研究调查了使用大型语言模型（LLMs）来解决自动生成 IaC 配置的问题的可行性，并深入探讨了 IaC 的细节、不同平台上的使用、挑战以及 LLMs 在代码生成方面的重要性，同时提出了这一领域的挑战并强调了未来研究的范围。

利用大型语言模型生成基础设施代码的调查

A Survey of using Large Language Models for Generating Infrastructure as  Code

We present LaMPilot, a novel framework for planning in the field of
autonomous driving, rethinking the task as a code-generation process that
leverages established behavioral primitives. This approach aims to address the
challenge of interpreting and executing spontaneous user instructions such as
"overtake the car ahead," which have typically posed difficulties for existing
frameworks. We introduce the LaMPilot benchmark specifically designed to
quantitatively evaluate the efficacy of Large Language Models (LLMs) in
translating human directives into actionable driving policies. We then evaluate
a wide range of state-of-the-art code generation language models on tasks from
the LaMPilot Benchmark. The results of the experiments showed that GPT-4, with
human feedback, achieved an impressive task completion rate of 92.7% and a
minimal collision rate of 0.9%. To encourage further investigation in this
area, our code and dataset will be made available.

我们提出了 LaMPilot，一个新颖的自主驾驶规划框架，将任务重新定义为一个利用已建立的行为基元的代码生成过程，旨在解决解释和执行 spontanous 用户指令（如 “超过前面的车”）的挑战，这通常对现有框架构成困难。我们介绍了 LaMPilot 基准，专门设计用于量化大型语言模型（LLMs）在将人类指令转化为可执行驾驶策略方面的效能。我们还在 LaMPilot 基准的任务上评估了一系列最先进的代码生成语言模型。实验结果显示，带有人类反馈的 GPT-4​​的任务完成率达到了 92.7％，最小碰撞率为 0.9％。为了鼓励进一步的研究，我们将提供代码和数据集。