What Quality Aspects Influence the Adoption of Docker Images?

Author:

Rosa Giovanni1ORCID,Scalabrino Simone1ORCID,Bavota Gabriele2ORCID,Oliveto Rocco1ORCID

Affiliation:

1. University of Molise, Italy

2. USI Universitá della Svizzera Italiana, Switzerland

Abstract

Docker is a containerization technology that allows developers to ship software applications along with their dependencies in Docker images. Developers can extend existing images using them as base images when writing Dockerfiles. However, a lot of alternative functionally equivalent base images are available. Although many studies define and evaluate quality features that can be extracted from Docker artifacts, the criteria on which developers choose a base image over another remain unclear. In this article, we aim to fill this gap. First, we conduct a literature review through which we define a taxonomy of quality features, identifying two main groups: configuration-related features (i.e., mainly related to the Dockerfile and image build process), and externally observable features (i.e., what the Docker image users can observe). Second, we ran an empirical study considering the developers’ preference for 2,441 Docker images in 1,911 open source software projects. We want to understand how the externally observable features influence the developers’ preferences, and how they are related to the configuration-related features. Our results pave the way to the definition of a reliable quality measure for Docker artifacts, along with tools that support developers for a quality-aware development of them.

Publisher

Association for Computing Machinery (ACM)

Subject

Software

Reference42 articles.

1. GitHub. 2015. Hadolint: Dockerfile Linter Validate Inline Bash Written in Haskell. Retrieved June 2 2022 from https://github.com/hadolint/hadolint.

2. Babak Amin Azad, Pierre Laperdrix, and Nick Nikiforakis. 2019. Less is more: Quantifying the security benefits of debloating web applications. In Proceedings of the 28th USENIX Security Symposium (USENIX Security’19). 1697–1714.

3. An empirical study on self-admitted technical debt in Dockerfiles

4. DockerFinder: Multi-attribute Search of Docker Images

5. An Empirical Analysis of the Docker Container Ecosystem on GitHub

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