DeepIaC: Deep learning-based linguistic anti-pattern detection in IaC

N. Borovits, Indika Weerasingha Dewage, P. Krishnan, S. Dalla Palma, D. Di Nucci, Fabio Palomba, Damian A. Tamburri, W.-J. Van Den Heuvel

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

14 Citations (Scopus)


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 between 0.785 and 0.915 in detecting inconsistencies.
Original languageEnglish
Title of host publicationMaLTeSQuE 2020 - Proceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation, Co-located with ESEC/FSE 2020
Publication statusPublished - Nov 2020


Dive into the research topics of 'DeepIaC: Deep learning-based linguistic anti-pattern detection in IaC'. Together they form a unique fingerprint.

Cite this