Automated Terraform Generation using NLP and Graph-Based Cloud Architecture Visualization

With the rapid advancement of cloud computing technologies, the management and provisioning of cloud infrastructure have become increasingly complex. The adoption of Infrastructure as Code (IaC) tools, such as Terraform, has streamlined cloud resource management. However, the manual creation of Terraform configuration files remains a challenging task that requires significant expertise in both cloud architecture and Terraform syntax. This paper presents an innovative approach to automating Terraform file generation using Natural Language Processing (NLP) and graph-based cloud architecture visualization. Our system enables users to describe their cloud infrastructure using natural language or through a graphical drag-and-drop interface. By integrating topological sorting techniques, our solution ensures the correctness of dependencies within the cloud architecture before generating Terraform files. The experimental results demonstrate that our approach enhances efficiency by reducing configuration time by up to 60%, minimizes human error in complex architectures, and makes Terraform more accessible to users with varying levels of expertise. This research contributes to the growing field of automated cloud infrastructure management by bridging the gap between human-readable descriptions and machine-executable infrastructure code. Index Terms—Terraform, Natural Language Processing, Graph-Based Visualization, Cloud Architecture, Topological Sorting, Infrastructure as Code