Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Dataset of Dockerfiles (2003.12912v1)

Published 28 Mar 2020 in cs.SE

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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Jordan Henkel (9 papers)
  2. Christian Bird (13 papers)
  3. Shuvendu K. Lahiri (32 papers)
  4. Thomas Reps (40 papers)
Citations (11)

Summary

We haven't generated a summary for this paper yet.