Harnessing the Power of LLMs for Code Smell Detection in Terraform Infrastructure as Code

Terraform is a widely used Infrastructure as Code (IaC) tool that simplifies cloud resource management through declarative configuration. However, Terraform configurations often exhibit code smells, which can introduce security vulnerabilities, maintainability challenges, and operational inefficiencies. While code smells have been extensively studied in other IaC platforms, research on Terraform remains limited, and existing static analysis tools struggle to detect a broad range of code smells. In this paper, we harness large language models (LLMs) for automated Terraform code smell detection, leveraging their ability to generalize beyond predefined rule-based heuristics. We construct a synthetic benchmark dataset of 42 Terraform configurations covering 14 distinct code smells and evaluate detection performance across traditional linters and LLM-based approaches. Our results show that LLMs significantly outperform static analysis tools, detecting a broader range of code smells, especially logic-related code smells. We extend our analysis to real-world repositories with 71 high-quality Terraform projects from GitHub. Our findings reveal that 84.5% of repositories contain at least one code smell. Notably, some repositories accumulate a high number of unique code smells, with one exhibiting nine distinct issues, underscoring severe quality concerns. Moreover, we find that code smells persist across repositories regardless of popularity, affecting even widely used projects with thousands of stars and forks.