Automated Bug Discovery in Cloud Infrastructure-as-Code Updates with LLM Agents

Cloud environments are increasingly managed by Infrastructure-as-Code (IaC) platforms (e.g., Terraform), which allow developers to define their desired infrastructure as a configuration program that describes cloud resources and their dependencies. This shields developers from low-level operations for creating and maintaining resources, since they are automatically performed by IaC platforms when compiling and deploying the configuration. However, while IaC platforms are rigorously tested for initial deployments, they exhibit myriad errors for runtime updates, e.g., adding/removing resources and dependencies. IaC updates are common because cloud infrastructures are long-lived but user requirements fluctuate over time. Unfortunately, our experience shows that updates often introduce subtle yet impactful bugs. The update logic in IaC frameworks is hard to test due to the vast and evolving search space, which includes diverse infrastructure setups and a wide range of provided resources with new ones frequently added. We introduce TerraFault, an automated, efficient, LLM-guided system for discovering update bugs, and report our findings with an initial prototype. TerraFault incorporates various optimizations to navigate the large search space efficiently and employs techniques to accelerate the testing process. Our prototype has successfully identified bugs even in simple IaC updates, showing early promise in systematically identifying update bugs in today's IaC frameworks to increase their reliability.

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Publication Details

Published
2025-05-03
DOI
https://doi.org/10.1109/aiops66738.2025.00011
Citations
1
Primary Topic
Software Testing and Debugging Techniques
Type
article
Field-Weighted Citation Impact
2.31

Funders

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article

Automated Bug Discovery in Cloud Infrastructure-as-Code Updates with LLM Agents

Yiming Xiang, Patrick Tser Jern Kon, Zhenning Yang, Ang Chen et al.
1 citations
Software Testing and Debugging Techniques
2.31
article

Automated Bug Discovery in Cloud Infrastructure-as-Code Updates with LLM Agents

Yiming Xiang, Patrick Tser Jern Kon, Zhenning Yang, Ang Chen, Peng Jin, Yiming Qiu, Hannelore Bauer
article en
1 citations

Abstract

Cloud environments are increasingly managed by Infrastructure-as-Code (IaC) platforms (e.g., Terraform), which allow developers to define their desired infrastructure as a configuration program that describes cloud resources and their dependencies. This shields developers from low-level operations for creating and maintaining resources, since they are automatically performed by IaC platforms when compiling and deploying the configuration. However, while IaC platforms are rigorously tested for initial deployments, they exhibit myriad errors for runtime updates, e.g., adding/removing resources and dependencies. IaC updates are common because cloud infrastructures are long-lived but user requirements fluctuate over time. Unfortunately, our experience shows that updates often introduce subtle yet impactful bugs. The update logic in IaC frameworks is hard to test due to the vast and evolving search space, which includes diverse infrastructure setups and a wide range of provided resources with new ones frequently added. We introduce TerraFault, an automated, efficient, LLM-guided system for discovering update bugs, and report our findings with an initial prototype. TerraFault incorporates various optimizations to navigate the large search space efficiently and employs techniques to accelerate the testing process. Our prototype has successfully identified bugs even in simple IaC updates, showing early promise in systematically identifying update bugs in today's IaC frameworks to increase their reliability.

University of Michigan (US)
National Science Foundation
Industry, innovation and infrastructure
Openalex Percentile: Top 13%
Software Testing and Debugging Techniques
2.31
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Automated Bug Discovery in Cloud Infrastructure-as-Code Updates with LLM Agents — Yiming Xiang, Patrick Tser Jern Kon, et al. · (2025) | TGRS Research Map | TGRS