An Empirical Analysis of the Docker Container Ecosystem on GitHub

Cito, Jürgen; Schermann, Gerald; Wittern, John Erik; Leitner, Philipp; Zumberi, Sali; Gall, Harald C.

doi:10.1109/msr.2017.67

Cited by 123 publications

(94 citation statements)

References 14 publications

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“…Selecting a threshold of 500 files further reduces the corpus to 145,876 projects. 8. https://cloud.google.com/bigquery/public-data/github…”

Section: Df1: Select Active and Large Projectsmentioning

confidence: 99%

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Use and Misuse of Continuous Integration Features: An Empirical Study of Projects That (Mis)Use Travis CI

Gallaba¹,

McIntosh²

2020

IIEEE Trans. Software Eng.

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Continuous Integration (CI) is a popular practice where software systems are automatically compiled and tested as changes appear in the version control system of a project. Like other software artifacts, CI specifications require maintenance effort. Although there are several service providers like TRAVIS CI offering various CI features, it is unclear which features are being (mis)used. In this paper, we present a study of feature use and misuse in 9,312 open source systems that use TRAVIS CI. Analysis of the features that are adopted by projects reveals that explicit deployment code is rare-48.16% of the studied TRAVIS CI specification code is instead associated with configuring job processing nodes. To analyze feature misuse, we propose HANSEL-an anti-pattern detection tool for TRAVIS CI specifications. We define four anti-patterns and HANSEL detects anti-patterns in the TRAVIS CI specifications of 894 projects in the corpus (9.60%), and achieves a recall of 82.76% in a sample of 100 projects. Furthermore, we propose GRETEL-an anti-pattern removal tool for TRAVIS CI specifications, which can remove 69.60% of the most frequently occurring anti-pattern automatically. Using GRETEL, we have produced 36 accepted pull requests that remove TRAVIS CI anti-patterns automatically.

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“…Selecting a threshold of 500 files further reduces the corpus to 145,876 projects. 8. https://cloud.google.com/bigquery/public-data/github…”

Section: Df1: Select Active and Large Projectsmentioning

confidence: 99%

“…Jha et al [17] proposed a static analysis tool for detecting errors in configuration files of ANDROID apps. In an exploratory empirical study, Cito et al [8] assessed the quality of DOCKER configuration files on GITHUB, observing that they violate 3.1 linter rules on average.…”

Section: Software Configuration Smellsmentioning

confidence: 99%

Use and Misuse of Continuous Integration Features: An Empirical Study of Projects That (Mis)Use Travis CI

Gallaba¹,

McIntosh²

2020

IIEEE Trans. Software Eng.

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“…Cito et al [9] conduct an empirical study on over 70,000 Dockerfiles assessing their quality and identify types of changes between consecutive versions of Dockerfiles. They show that common Dockerfiles are not changed often, while they indicate that popular Dockerfiles (like the ones in our corpus) happen to be more actively updated.…”

Section: Related Workmentioning

confidence: 99%

Handling Duplicates in Dockerfiles Families: Learning from Experts

Oumaziz¹,

Falleri²,

Blanc³

et al. 2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Docker is becoming a popular tool used by developers and end-users to deploy and run software applications. Dockerfiles are now found alongside projects' source code. Several projects are even starting to maintain families of Dockerfiles, like the Python project that maintains a family of 43 Dockerfiles, each for a specific Python version on a specific Linux distribution. In some situations, Dockerfiles family maintainers have to propagate a change to several, if not all, Dockerfiles of the family (for instance a bugfix applying on all Dockerfiles targeting Python 2.7). This need to propagate changes is usually due to the presence of duplicates between several Dockerfiles of the family. In this paper, our goal is to provide practitioners a clear explanation for why Dockerfile duplicates arise in projects, and what are the different means to handle duplicates with their pros and cons. To perform a grounded analysis, we observe the practices of expert Dockerfile maintainers of Official Docker projects, which are setting the best-practices for Dockerfile maintenance. We show that duplicates in Dockerfiles are frequent in our corpus, that maintainers are aware of their existence, are frequently facing them and have a mixed opinion regarding them (error-prone when not using any tool, but easy to maintain with the right tools). Finally, we describe and analyse the tools used by maintainers to handle duplicates.

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“…Our search has been performed on the publicly-available GitHub mirror available in BigQuery. 4 This mirror contains information about files in all open-source repositories on GitHub, making it a good interface for finding repositories containing certain file types [3]. Our query returned 378,610 AndroidManifest.xml files across 124,068 repositories (search performed in October 2017).…”

Section: Apps Identificationmentioning

confidence: 99%

A graph-based dataset of commit history of real-world Android apps

Geiger

Malavolta

Pascarella

et al. 2018

Proceedings of the 15th International Conference on Mining Software Repositories

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Obtaining a good dataset to conduct empirical studies on the engineering of Android apps is an open challenge. To start tackling this challenge, we present AndroidTimeMachine, the first, self-contained, publicly available dataset weaving spread-out data sources about real-world, open-source Android apps. Encoded as a graph-based database, AndroidTimeMachine concerns 8,431 real open-source Android apps and contains: (i) metadata about the apps' GitHub projects, (ii) Git repositories with full commit history and (iii) metadata extracted from the Google Play store, such as app ratings and permissions.

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An Empirical Analysis of the Docker Container Ecosystem on GitHub

Cited by 123 publications

References 14 publications

Use and Misuse of Continuous Integration Features: An Empirical Study of Projects That (Mis)Use Travis CI

Use and Misuse of Continuous Integration Features: An Empirical Study of Projects That (Mis)Use Travis CI

Handling Duplicates in Dockerfiles Families: Learning from Experts

A graph-based dataset of commit history of real-world Android apps

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