TerraMetrics: An Open Source Tool for Infrastructure-as-Code (IaC) Quality Metrics in Terraform
Bibliographic record
Abstract
Infrastructure-as-Code (IaC) constitutes a pivotal DevOps methodology, leading edge of software deployment onto cloud platforms. IaC relies on source code files rather than manual configuration to manage the infrastructure of a software system. Terraform, an IaC tool and its declarative configuration language named HCL, has recently garnered considerable attention among IaC practitioners. Like other software artefacts, Terraform files could be affected by misconfigurations, faults, and smells. Therefore, DevOps practitioners might benefit from a quality assurance tool to help them perform quality assurance activities on Terrafrom artefacts. This paper introduces TerraMetrics, an open-source tool designed to characterize the quality of Terraform artefacts by providing a catalogue of 40 quality metrics. TerraMetrics leverages the Terraform Abstract Syntax Tree (AST) to extract the metric list, offering a potentially enduring solution compared to conventional regular expressions. This tool comprises three main components: (i) a parser transforming HCL code into an AST, (ii) visitors that traverse the AST nodes to extract the metrics, and (iii) collectors for storing the collected metrics in JSON format. The TerraMetrics tool is publicly available as an Open Source tool, with a demo video, at: https://github.com/stilab-ets/terametrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".