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
@article{arxiv.2009.10801,
title = {DeepIaC: Deep Learning-Based Linguistic Anti-pattern Detection in IaC},
author = {Nemania Borovits and Indika Kumara and Parvathy Krishnan and Stefano Dalla Palma and Dario Di Nucci and Fabio Palomba and Damian A. Tamburri and Willem-Jan van den Heuvel},
journal= {arXiv preprint arXiv:2009.10801},
year = {2020}
}