The results show that most of the activities of the process of developing a Dockerfile are perceived as time-consuming, especially when the respondents are beginners with this technology.
#DOCKSTATION DOCKERFILE SOFTWARE#
The first part of the study reached 68 graduate students from a study program on informatics engineering, and the second one 120 professional software developers. We used an online questionnaire to gather data. We seek to know how they can be better supported and therefore study also what approaches and tools practitioners employ. This work aims to characterize the activities around two particular kinds of IaC specification-Dockerfiles and docker-compose.yml files. Previous research has identified typical issues for some types of IaC specification but not why they come to be, or they have delved into collaboration aspects but not into technical ones. It is further recommended that while researchers should strive for more representative samples, disparaging non-probability sampling is generally capricious and particularly misguided for predominately qualitative research.Ĭloud computing and Infrastructure-as-Code (IaC), supported by technologies such as Docker, have shaped how many software systems are built and deployed. To address these problems, this paper synthesizes existing knowledge of sampling into a succinct primer and proposes extensive guidelines for improving the conduct, presentation and evaluation of sampling in software engineering research. These findings suggest that software engineering research has a generalizability crisis. The key findings are: (1) random sampling is rare (2) sophisticated sampling strategies are very rare (3) sampling, representativeness and randomness often appear misunderstood. This article therefore reports a critical review of the state of sampling in recent, high-quality software engineering research. Not all studies need representative samples, but a general lack of representative sampling undermines a scientific field. Representative sampling appears rare in empirical software engineering research. The participants also thought the prototype easier to use and useful, and wanted to use it in the future. Results: The results show a significant reduction in development time and error-proneness when defining Docker Compose files, supporting our hypothesis. Therefore, we developed a tool featuring such a visual notation for Docker Compose configurations, and we empirically evaluated it in a controlled experiment with novice developers. Goal & method: We hypothesize that a complete visual notation for Docker-based orchestrations could reduce the effort, the error rate, and the development time. We can find few examples of low-code approaches for defining the orchestration of containers, and there seems to be a lack of empirical studies showing the benefits and limitations of such approaches. Still, their text-based nature permits naive mistakes and is more difficult to read as complexity increases. Context: Container orchestration tools supporting infrastructure-as-code allow new forms of collaboration between developers and operatives.
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