# DeepIaC: Deep Learning-Based Linguistic Anti-pattern Detection in IaC

arXiv: https://arxiv.org/abs/2009.10801 · HTML: https://mlsanity.com/paper/2009.10801/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Security smells in infrastructure as code: a taxonomy update beyond the seven sins](https://mlsanity.com/paper/2509.18761/) (2025)
- [Detection of security smells in IaC scripts through semantics-aware code and language processing](https://mlsanity.com/paper/2509.18790/) (2025)

## Abstract (arXiv)

Linguistic anti-patterns are recurring poor practices concerning inconsistencies among the naming, documentation, and implementation of an entity. They impede readability, understandability, and maintainability of source code. This paper attempts to detect linguistic anti-patterns in infrastructure as code (IaC) scripts used to provision and manage computing environments. In particular, we consider inconsistencies between the logic/body of IaC code units and their names. To this end, we propose a novel automated approach that employs word embeddings and deep learning techniques. We build and use the abstract syntax tree of IaC code units to create their code embedments. Our experiments with a dataset systematically extracted from open source repositories show that our approach yields an accuracy between0.785and0.915in detecting inconsistencies