Dockerfile plays an important role in the Docker-based software development process, but many Dockerfile codes are infected with smells in practice. Understanding the occurrence of Dockerfile smells in open-source software can benefit the practice of Dockerfile and enhance project maintenance. In this paper, we perform an empirical study on a large dataset of 6,334 projects to help developers gain some insights into the occurrence of Dockerfile smells, including its coverage, distribution, cooccurrence, and correlation with project characteristics. Our results show that smells are very common in Dockerfile codes and there exists co-occurrence between different types of Dockerfile smells. Further, using linear regression analysis, when controlled for various variables, we statistically identify and quantify the relationships between Dockerfile smells occurrence and project characteristics. We also provide a rich resource of implications for software practitioners.

Continuous integration, deployment and delivery (CI/CD) have become cornerstones of DevOps practices. In recent years, GitHub Action (GHA) has rapidly replaced the traditional CI/CD tools on GitHub, providing efficiently automated workflows for developers. With the widespread use and influence of GHA, it is critical to understand the existing problems that GHA developers face in their practices as well as the potential solutions to these problems. Unfortunately, we currently have relatively little knowledge in this area. To fill this gap, we conduct a large-scale empirical study of 6,590 Stack Overflow (SO) questions and 315 GitHub issues. Our study leads to the first comprehensive taxonomy of problems related to GHA, covering 4 categories and 16 sub-categories. Then, we analyze the popularity and difficulty of problem categories and their correlations. Further, we summarize 56 solution strategies for different GHA problems. We also distill practical implications of our findings from the perspective of different audiences. We believe that our study contributes to the research of emerging GHA practices and guides the future support of tools and technologies.

Docker building is a critical component of containerization in modern software development, automating the process of packaging and converting sources into container images. It is not uncommon to find that Docker build faults (DBFs) occur frequently across Docker-based projects, inducing non-negligible costs in development activities. DBF resolution is a challenging problem and previous studies have demonstrated that developers spend non-trivial time in resolving encountered build faults. However, the characteristics of DBFs is still under-investigated, hindering practical solutions to build management in the Docker community. In this paper, to bridge this gap, we present a comprehensive study for understanding the real-world DBFs in practice. We collect and analyze a DBF dataset of 255 issues and 219 posts from GitHub, Stack Overflow, and Docker Forum. We investigate and construct characteristic taxonomies for the DBFs, including 15 symptoms, 23 root causes, and 35 fix patterns. Moreover, we study the fault distributions of symptoms and root causes, in terms of the different build types, i.e., Dockerfile builds and Docker-compose builds. Based on the results, we provide actionable implications and develop a knowledge-based application, which can potentially facilitate research and assist developers in improving the Docker build management.