Findici: using machine learning to detect linguistic inconsistencies between code and natural language descriptions in infrastructure-as-code

HIGHLIGHTS

• who: Nemania Borovits from the Jheronimus Academy of Data Science, Tilburg University, Tilburg, The Netherlands have published the paper: FindICI: Using machine learning to detect linguistic inconsistencies between code and natural language descriptions in infrastructure-as-code, in the Journal: (JOURNAL)
• what: The authors propose FINDICI a novel automated approach that employs word embedding and classification algorithms. The first steps in this direction focused on applying the well-known concept of software defect prediction (Hall et_al 2011) to infrastructure code defining defect prediction models (Rahman and Williams 2018, 2019a; Dalla Palma et_al 2021) to identify . . .
  

   

  

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