A Survey of using Large Language Models for Generating Infrastructure as
  Code
Kalahasti Ganesh Srivatsa Sabyasachi Mukhopadhyay Ganesh Katrapati Manish Shrivastava
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.