DeepIaC - Deep Learning-Based Linguistic Anti-pattern Detection in IaC.

Nemania Borovits, Indika Kumara, Parvathy Krishnan, Stefano Dalla Palma, Dario Di Nucci, Fabio Palomba, Damian A. Tamburri, Willem-Jan van den Heuvel

Research output: Other contributionAcademic

13 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
Number of pages6
ISBN (Electronic)9781450381246
Publication statusPublished - 13 Nov 2020


  • Code Embedding
  • Deep Learning
  • Defects
  • IaC
  • Infrastructure Code
  • Linguistic Anti-patterns
  • Word2Vec


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