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

TL;DR

This survey explores the potential of Large Language Models to automate Infrastructure as Code generation, addressing manual effort and skill requirements, and discusses current challenges and future research directions.

Contribution

It provides a comprehensive overview of applying LLMs to IaC, including experiments, challenges, and future research scope, which is a novel synthesis in this domain.

Findings

LLMs can effectively generate IaC configurations.

Applying LLMs reduces manual effort in IaC.

Identified key challenges and future research directions.

Abstract

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.