A Dataset of Dockerfiles

March 28, 2020 Β· Declared Dead Β· πŸ› IEEE Working Conference on Mining Software Repositories

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Authors Jordan Henkel, Christian Bird, Shuvendu K. Lahiri, Thomas Reps arXiv ID 2003.12912 Category cs.SE: Software Engineering Citations 14 Venue IEEE Working Conference on Mining Software Repositories Last Checked 4 months ago
Abstract
Dockerfiles are one of the most prevalent kinds of DevOps artifacts used in industry. Despite their prevalence, there is a lack of sophisticated semantics-aware static analysis of Dockerfiles. In this paper, we introduce a dataset of approximately 178,000 unique Dockerfiles collected from GitHub. To enhance the usability of this data, we describe five representations we have devised for working with, mining from, and analyzing these Dockerfiles. Each Dockerfile representation builds upon the previous ones, and the final representation, created by three levels of nested parsing and abstraction, makes tasks such as mining and static checking tractable. The Dockerfiles, in each of the five representations, along with metadata and the tools used to shepard the data from one representation to the next are all available at: https://doi.org/10.5281/zenodo.3628771.
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