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.

Function as a Service (FaaS) has grown in popularity in recent years, with an increasing number of applications following the Serverless computing model. Serverless computing supports out of the box autoscaling in a pay-as-you-go manner, letting developers focus on the application logic rather than worrying about resource management. With the increasing adoption of the this model, researchers have started studying a wide variety of aspects of Serverless computing, including communication, security, performance, and cost optimization. Yet, we still know very little of how Serverless computing is used in practice.In this paper, we introduce Wonderless, a novel dataset of open-source Serverless applications. Wonderless consists of 1,877 real-world Serverless applications extracted from GitHub, and it can be used as a data source for further research in the Serverless ecosystem, such as performance evaluation and software mining. To the best of our knowledge, Wonderless is currently the most diverse and largest dataset for research on Serverless computing.