Assessing and Improving the Quality of Docker Artifacts

Abstract

Docker is the most diffused containerization technology adopted in the DevOps workflow. Docker allows shipping applications in Docker images, along with their dependencies and execution environment. A Docker image is created using a configuration file called Dockerfile. The literature shows that quality issues, such as violations of best practices (i.e., Dockerfile smells), are diffused among Docker artifacts. Smells can negatively impact the reliability, leading to building failures, poor performance, and security issues. In addition, it is unclear to what extent developers are aware of those quality issues and what quality aspects are correlated with the adoption of a Docker image. As evaluated in the literature, composing high-quality Dockerfiles and Docker images is not a trivial task. In this research, we aim to propose approaches and techniques to assess and improve the quality of Dockerfiles and Docker images. First, starting from the resolution of Dockerfile smells, we aim to improve the internal and then the related external quality aspects that also affect the developers’ preference and the perceived quality when they adopt a Docker image. Next, we want to employ that knowledge in the automated generation of high-quality Dockerfiles and Docker images.