Refactoring for Dockerfile Quality: A Dive into Developer Practices and Automation Potential

TL;DR

This paper investigates automating Dockerfile refactoring using AI techniques, demonstrating significant reductions in image size and build time, and improved maintainability, thereby proposing a practical approach for continuous Dockerfile quality enhancement.

Contribution

It introduces an AI-based automated refactoring method for Dockerfiles, showing substantial improvements over manual and existing tools, and discusses its integration into CI/CD pipelines.

Findings

Average 32% reduction in image size

6% decrease in build duration

Improved understandability and maintainability in most cases

Abstract

Docker, the industry standard for packaging and deploying applications, leverages Infrastructure as Code (IaC) principles to facilitate the creation of images through Dockerfiles. However, maintaining Dockerfiles presents significant challenges. Refactoring, in particular, is often a manual and complex process. This paper explores the utility and practicality of automating Dockerfile refactoring using 600 Dockerfiles from 358 open-source projects. Our study reveals that Dockerfile image size and build duration tend to increase as projects evolve, with developers often postponing refactoring efforts until later stages in the development cycle. This trend motivates the automation of refactoring. To achieve this, we leverage In Context Learning (ICL) along with a score-based demonstration selection strategy. Our approach leads to an average reduction of 32% in image size and a 6% decrease in build duration, with improvements in understandability and maintainability observed in 77% and 91% of cases, respectively. Additionally, our analysis shows that automated refactoring reduces Dockerfile image size by 2x compared to manual refactoring and 10x compared to smell-fixing tools like PARFUM. This work establishes a foundation for automating Dockerfile refactoring, indicating that such automation could become a standard practice within CI/CD pipelines to enhance Dockerfile quality throughout every step of the software development lifecycle.