Development of Code Completion System for Dockerfiles

Abstract

Containerization, in which multiple virtual servers (i.e., containers) are built on a single physical server, is widely employed for cost reduction and effective resource utilization. The object of this study is Docker, the de facto standard containerization platform. Containers in Docker are built by writing configuration scripts and creating files called Dockerfile. Managing the infrastructure as code makes it possible to apply knowledge gained from conventional software development to infrastructure configuration. However, infrastructure as code is a relatively new technology, some domains of which have not been fully researched. In this study, we focus on code completion and aim to construct a system that supports the development of dfs. The proposed system applies machine learning with long short-term memory to a pre-collected dataset to create language models and uses model switching to overcome a Docker-specific code completion problem. Evaluation experiments show that the implemented code completion system, hb, has a high average recommendation accuracy of 88.9%.