Fixing Dockerfile Smells: An Empirical Study

TL;DR

This study investigates the prevalence and fixability of Dockerfile smells in open-source projects, evaluating developer willingness to accept automated fixes and identifying which smells are most addressed.

Contribution

It provides empirical insights into Dockerfile smell fixing behaviors and assesses the potential for automated tools to support developers in improving Dockerfile quality.

Findings

Dockerfile smells are frequently fixed in open-source projects

Developers show willingness to accept automated fixes for Dockerfile smells

Certain Dockerfile smells are more persistent and less likely to be fixed

Abstract

Background. Containerization technologies are widely adopted in the DevOps workflow. The most commonly used one is Docker, which requires developers to define a specification file (Dockerfile) to build the image used for creating containers. There are several best practice rules for writing Dockerfiles, but the developers do not always follow them. Violations of such practices, known as Dockerfile smells, can negatively impact the reliability and the performance of Docker images. Previous studies showed that Dockerfile smells are widely diffused, and there is a lack of automatic tools that support developers in fixing them. However, it is still unclear what Dockerfile smells get fixed by developers and to what extent developers would be willing to fix smells in the first place. Objective. The aim of our exploratory study is twofold. First, we want to understand what Dockerfiles smells receive more attention from developers, i.e., are fixed more frequently in the history of open-source projects. Second, we want to check if developers are willing to accept changes aimed at fixing Dockerfile smells (e.g., generated by an automated tool), to understand if they care about them. Method. In the first part of the study, we will evaluate the survivability of Dockerfile smells on a state-of-the-art dataset composed of 9.4M unique Dockerfiles. We rely on a state-of-the-art tool (hadolint) for detecting which Dockerfile smells disappear during the evolution of Dockerfiles, and we will manually analyze a large sample of such cases to understand if developers fixed them and if they were aware of the smell. In the second part, we will detect smelly Dockerfiles on a set of GitHub projects, and we will use a rule-based tool to automatically fix them. Finally, we will open pull requests proposing the modifications to developers, and we will quantitatively and qualitatively evaluate their outcome.