Infrastructure-as-Code (IaC) engines, such as Terraform, OpenTofu, and Pulumi, automate the provisioning and management of cloud resources. They parse IaC specifications and orchestrate the required actions, making them the backbone of modern clouds, and critical to the reliability of both the underlying infrastructure and the software that depends on it. Despite this importance, this class of systems has received little attention: prior work largely targets the correctness of IaC programs rather than the IaC engines themselves. Existing test suites rely on manually written oracles and struggle to expose faults that manifest across multiple executions, leaving a significant reliability gap. We present EMI a C, a metamorphic testing framework for IaC engines. EMI a C defines metamorphic relations as graph-based transformations of IaC programs and checks invariants across executions of the original and transformed programs. A central novelty is our use of e-graphs in software testing, as both a test-input generator and an equivalence oracle. E-graphs compactly represent program equivalences, enabling the systematic generation of large spaces of equivalent IaC programs. To ground these relations, we analyze 43,593 real-world Terraform programs and show that IaC dependency graphs are typically small and sparse, making e-graphs a natural fit. Evaluating EMI a C on Pulumi, Terraform, and OpenTofu, we show that it complements existing test suites by exercising engine-critical code paths and covering 98 previously untested statements in Terraform and 1,313 in Pulumi. EMI a C also uncovers previously unknown issues in all three test suites, improving their adequacy. Three test cases have been merged into Terraform’s main branch, and Pulumi has merged a specification fix.

Infrastructure as Code (IaC) aims to automate infrastructure management by enabling the definition of infrastructure configurations in programs, rather than manually configuring hardware or cloud resources. Terraform is one of the most widely used IaC tools, gaining significant traction in recent years, as highlighted by its large and active user community and widespread adoption in both open-source and enterprise environments. Terraform’s code is written in the HashiCorp Configuration Language (HCL), which defines the infrastructure in a declarative manner. Despite the widespread adoption of Terraform, there is no large-scale dataset available for researchers to study IaC Terraform programs systematically. To address this gap, we present TerraDS, the first dataset of publicly available Terraform programs written in HCL. TerraDS contains the HCL code and the metadata of 67,360 open source repositories with permissive open-source licenses. The dataset includes 279,344 Terraform modules with 1,773,991 registered resources, all compiled into a reusable archive ($\sim 335 \mathrm{MB}$).

With Programming Languages Infrastructure as Code (PL-IaC), developers implement IaC programs in popular imperative programming languages like Python and Typescript. Such programs generate the declarative target state of the deployment, i.e., they describe what to set up, not how to set it up. Despite the popularity of PL-IaC, which has grown more than ten times from 2020 to 2023, we know little about how developers apply it and how IaC programs differ from other software. Such knowledge is essential to effectively use existing software engineering techniques and develop new ones for PL-IaC. To shed light on PL-IaC in practice, we present PIPr, the first systematic PL-IaC dataset. PIPr is based on 37 712 public IaC programs on GitHub from August 2022 and includes initial analyses, assessing the programming languages, testing techniques, and licenses of the IaC programs. Beyond the metadata and analysis results of all IaC programs, PIPr contains the code of all 15 504 IaC programs whose licenses permit redistribution. PIPr sets the ground for future in-depth investigations on PL-IaC in practice.

Infrastructure as Code (IaC) enables efficient deployment and operation, which are crucial to releasing software quickly. As setups can be complex, developers implement IaC programs in general-purpose programming languages like TypeScript and Python, using PL-IaC solutions like Pulumi and AWS CDK. The reliability of such IaC programs is even more relevant than in traditional software because a bug in IaC impacts the whole system. Yet, even though testing is a standard development practice, it is rarely used for IaC programs. For instance, in August 2022, less than 1% of the public Pulumi IaC programs on GitHub implemented tests. Available IaC program testing techniques severely limit the development velocity or require much development effort.To solve these issues, we propose Automated Configuration Testing (ACT), a methodology to test IaC programs in many configurations quickly and with low effort. ACT automatically mocks all resource definitions in the IaC program and uses generator and oracle plugins for test generation and validation. We implement ACT inProTI, a testing tool for Pulumi TypeScript with a type-based generator and oracle, and support for application specifications. Our evaluation with 6 081 programs from GitHub and artificial benchmarks shows thatProTIcan directly be applied to existing IaC programs, quickly finds bugs where current techniques are infeasible, and enables reusing existing generators and oracles thanks to its pluggable architecture.