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

Author:

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

Abstract

AbstractLinguistic anti-patterns are recurring poor practices concerning inconsistencies in the naming, documentation, and implementation of an entity. They impede the 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 short text names. To this end, we propose FindICI a novel automated approach that employs word embedding and classification algorithms. We build and use the abstract syntax tree of IaC code units to create code embeddings used by machine learning techniques to detect inconsistent IaC code units. We evaluated our approach with two experiments on Ansible tasks systematically extracted from open source repositories for various word embedding models and classification algorithms. Classical machine learning models and novel deep learning models with different word embedding methods showed comparable and satisfactory results in detecting inconsistent Ansible tasks related to the top-10 used Ansible modules.

Funder

Horizon 2020 Framework Programme

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Ministero dell’Università e della Ricerca

Publisher

Springer Science and Business Media LLC

Subject

Software

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An empirical study of task infections in Ansible scripts;Empirical Software Engineering;2023-12-29

2. Deep learning with class-level abstract syntax tree and code histories for detecting code modification requirements;Journal of Systems and Software;2023-12

3. What Do Infrastructure-as-Code Practitioners Discuss: An Empirical Study on Stack Overflow;2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM);2023-10-26

4. Control and Data Flow in Security Smell Detection for Infrastructure as Code: Is It Worth the Effort?;2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR);2023-05

5. Detecting Inconsistencies in Software Architecture Documentation Using Traceability Link Recovery;2023 IEEE 20th International Conference on Software Architecture (ICSA);2023-03

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