How Do Infrastructure-as-Code Practitioners Update Their Dependencies? An Empirical Study on Terraform Module Updates
Bibliographic record
Abstract
Infrastructure-as-Code (IaC) enables practitioners to configure and manage software infrastructure through machine-readable code files. Various IaC tools facilitate code reuse and modularity via IaC modules that act as dependencies. These modules are maintained by IaC providers to introduce new features, resolve bugs, or address security vulnerabilities. However, there is a limited understanding of how practitioners update their IaC module dependencies in their software projects, including updates frequency, delays, as well as motivations behind such updates. To fill this gap, this paper aims to understand current update practices in IaC module dependencies, focusing on Terraform (TF), being currently one of the most popular IaC tools. In particular, we investigate (i) the frequency in which IaC practitioners update their module dependencies, (ii) the technical lag phenomena, which represents the time that the infrastructure configurations remain outdated relative to their upstream modules, and (iii) the motivations that drive these updates. To achieve these, we conduct an empirical study on 13,490 TF-related commits from 131 open-source projects. Our results reveal that only 1.2% of the analyzed commits involve updating module dependencies. Furthermore, we observe an increasing technical lag from 2021 until 2024, reaching ten months on average by 2024. Then, we conduct a qualitative study using thematic analysis on code changes involving TF module dependencies updates to investigate practitioners’ motivations behind such updates. We identify that TF practitioners revolve around six main motivations, with IaC Ecosystem Compatibility, Security Vulnerabilities Fixes, and IaC Code Quality Improvement being the three most prevalent motivations. Our findings advocate that TF practitioners need customized IaC tool support for safe module dependency updates while addressing compatibility concerns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".