ConfLang 2026 – Preliminary Table of Contents

3rd International Workshop on Configuration Languages (ConfLang 2026)

Frontmatter

Papers

Scopeannon: A Static Analyzer of Ansible’s Scope Ambiguities
Olivia Proust, Hélène Coullon, Jolan Philippe, and Frédéric Loulergue
(IMT Atlantique, France; Inria, France; LS2N, France; University of Orléans, France; INSA CVL, France; LIFO, France)
Nowadays, Infrastructure-as-Code (IaC) is widely used to manage large distributed software systems through scripts. However, script errors could lead to cascading failures rendering systems and applications unavailable. For this reason, the reliability and code quality of IaC systems have been extensively studied, highlighting different types of smells across tools. CAnsible, a prominent configuration management tool in the IaC ecosystem, is known for its unexpected semantics and its unintuitive variable scope management, which can result in misconfigurations. We introduce Scopeannon, a work in progress static analyzer for Ansible that aims to help practitioners better understand and handle this peculiar scope management.
Article Search Article: asews26conflangmain-p2-p (type: Short Paper (4 pages)) doi:10.1145/3844136.3845868

Poliac: Production Observability for LLM-Based Infrastructure as Code
Haitam El Hayani, Jolan Philippe, and Stéphanie Challita
(Inria, France; IRISA, France; University of Rennes, France; University of Orléans, France; INSA CVL, France; LIFO, France)
Infrastructure as Code (IaC) enables the automated provisioning of cloud infrastructures, yet syntactically valid configurations may remain unsuitable for the applications they support. Addressing such issues requires knowledge of both the infrastructure and its runtime behavior. We introduce POLIAC, an LLM-based framework that combines an IaC artifact, a natural-language specification, and aggregated operational data to generate infrastructure recommendations grounded in the state of the deployed system. We conduct a preliminary study on CorrectExam, a web application deployed on Grid'5000, collecting system- and application-level metrics under load. We query four LLMs with and without these operational aggregates. Without operational data, the models propose different resource modifications; when operational data are provided, all models recommend increasing vCPU while preserving memory, consistently with the observed CPU saturation and moderate memory utilization. These preliminary results suggest that operational data can reduce ambiguity in LLM-generated IaC recommendations and help avoid unnecessary resource provisioning.
Article Search Article: asews26conflangmain-p3-p (type: Short Paper (4 pages)) doi:10.1145/3844136.3845869

Déjà Vu? Redundant Dependencies in Terraform
Carlos Felgueiras, Luís W. Barbosa, Nuno Saavedra, Alexandra Mendes, and João F. Ferreira
(INESC-ID, Portugal; University of Lisbon, Portugal; University of Porto, Portugal; INESC TEC, Portugal)
Infrastructure as Code (IaC) enables practitioners to provision cloud infrastructure through declarative specifications. In Terraform, developers can define explicit dependencies to enforce execution order when implicit dependencies are insufficient. However, developers may explicitly declare dependencies that are already captured by expression references. Such redundant dependencies add unnecessary complexity and, more importantly, may cause Terraform to produce overly conservative plans and unnecessarily constrain the order in which infrastructure objects are provisioned.
To enable this study, we develop depends_off, a static analysis tool for detecting redundant dependencies in Terraform configurations, and use it to analyze 76,296 repositories comprising 226,662 modules. We investigate the prevalence and characteristics of redundant dependencies and their impact on Terraform dependency graphs. We detect 89,570 redundant dependencies, affecting 17% of repositories and 10% of modules. Redundancy is strongly associated with particular resource types and providers, but not with repository-level characteristics such as size, age, or activity. Moreover, removing possibly redundant dependencies reduces the critical path in 8.6% of the cases we could measure, with a median reduction of 14.3% among affected graphs. These findings establish redundant dependencies as a recurring and potentially consequential IaC quality issue and motivate their automated detection as part of Terraform development and maintenance.
Article Search Article: asews26conflangmain-p6-p (type: Full Paper) doi:10.1145/3844136.3845870

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