A Survey of using Large Language Models for Generating Infrastructure as Code
This is an incremental survey that reviews existing methods for applying LLMs to IaC automation, targeting the IT industry to reduce manual effort.
The survey explores the feasibility of using Large Language Models (LLMs) to automate Infrastructure as Code (IaC) generation, addressing the manual effort and specialized skills required, and concludes by identifying challenges and future research directions.
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.