论文标题
Deepiac：IAC中基于深度学习的语言抗模式检测
DeepIaC: Deep Learning-Based Linguistic Anti-pattern Detection in IaC
论文作者
论文摘要
语言反诉讼是关于实体的命名，文档和实施之间不一致的反复发生的。它们阻碍了源代码的可读性，可理解性和可维护性。本文试图将基础架构中的语言反图案视为用于提供和管理计算环境的代码（IAC）脚本。特别是，我们考虑IAC代码单元的逻辑/正文及其名称之间的矛盾之处。为此，我们提出了一种新型的自动化方法，该方法采用单词嵌入和深度学习技术。我们构建和使用IAC代码单元的抽象语法树来创建其代码嵌入。我们对从开源存储库中系统提取的数据集进行的实验表明，我们的方法得出的准确性在0.785and0.915in中检测不一致之处
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
