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  1. Stackups
  2. DevOps
  3. Continuous Integration
  4. Continuous Integration
  5. InSpec vs Test Kitchen

InSpec vs Test Kitchen

OverviewComparisonAlternatives

Overview

Test Kitchen
Test Kitchen
Stacks246
Followers45
Votes15
GitHub Stars1.9K
Forks582
InSpec
InSpec
Stacks336
Followers49
Votes0
GitHub Stars3.0K
Forks683

InSpec vs Test Kitchen: What are the differences?

Introduction

InSpec and Test Kitchen are both tools used in the field of infrastructure testing and automation. While they serve similar functions, there are some key differences between the two that set them apart.

  1. Purpose: InSpec is a testing framework that focuses on defining and enforcing compliance, security, and policy requirements within infrastructure code. Test Kitchen, on the other hand, is primarily used for the development and testing of infrastructure code and configuration management solutions before deployment.

  2. Language: InSpec uses a domain-specific language (DSL) specifically designed for writing compliance tests. Test Kitchen, on the other hand, typically works in conjunction with infrastructure provisioning tools such as Chef, Ansible, or Puppet, using the syntax of these tools to define infrastructure configurations.

  3. Testing Scope: InSpec is primarily used for integration testing of infrastructure and compliance requirements against actual nodes in a system. Test Kitchen, on the other hand, focuses on unit testing and validating the behavior of infrastructure code by spinning up virtual server instances and applying configurations.

  4. Dependencies: In the case of InSpec, the tool is standalone and does not have dependencies on other tools or frameworks. Test Kitchen, on the other hand, relies on external tools such as Chef, Ansible, or Puppet for infrastructure provisioning and configuration management.

  5. User Base: InSpec is more commonly used by compliance and security professionals for validating and auditing infrastructure compliance. Test Kitchen, on the other hand, is popular among developers and operations teams for testing infrastructure configurations in development environments.

  6. Execution Environment: InSpec tests are typically executed on target systems directly, while Test Kitchen runs tests on temporary instances or containers to simulate the production environment.

In Summary, despite sharing the goal of testing infrastructure code, InSpec and Test Kitchen differ in purpose, language, testing scope, dependencies, user base, and execution environment.

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Detailed Comparison

Test Kitchen
Test Kitchen
InSpec
InSpec

Test Kitchen has a static, declarative configuration in a .kitchen.yml file at the root of your project. It is designed to execute isolated code run in pristine environments ensuring that no prior state exists. A plugin architecture gives you the freedom to run your code on any cloud, virtualization, or bare metal resources and allows you to write acceptance criteria in whatever framework you desire.

It is an open-source testing framework for infrastructure with a human- and machine-readable language for specifying compliance, security and policy requirements.

Statistics
GitHub Stars
1.9K
GitHub Stars
3.0K
GitHub Forks
582
GitHub Forks
683
Stacks
246
Stacks
336
Followers
45
Followers
49
Votes
15
Votes
0
Pros & Cons
Pros
  • 6
    Automated testing
  • 4
    Detect bugs in cook books
  • 2
    Integrates well with vagrant
  • 2
    Can containerise tests in Docker
  • 1
    Integrates well with puppet
No community feedback yet
Integrations
No integrations available
Linux
Linux
Docker
Docker
Windows
Windows

What are some alternatives to Test Kitchen, InSpec?

Jenkins

Jenkins

In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.

Travis CI

Travis CI

Free for open source projects, our CI environment provides multiple runtimes (e.g. Node.js or PHP versions), data stores and so on. Because of this, hosting your project on travis-ci.com means you can effortlessly test your library or applications against multiple runtimes and data stores without even having all of them installed locally.

Codeship

Codeship

Codeship runs your automated tests and configured deployment when you push to your repository. It takes care of managing and scaling the infrastructure so that you are able to test and release more frequently and get faster feedback for building the product your users need.

CircleCI

CircleCI

Continuous integration and delivery platform helps software teams rapidly release code with confidence by automating the build, test, and deploy process. Offers a modern software development platform that lets teams ramp.

TeamCity

TeamCity

TeamCity is a user-friendly continuous integration (CI) server for professional developers, build engineers, and DevOps. It is trivial to setup and absolutely free for small teams and open source projects.

Drone.io

Drone.io

Drone is a hosted continuous integration service. It enables you to conveniently set up projects to automatically build, test, and deploy as you make changes to your code. Drone integrates seamlessly with Github, Bitbucket and Google Code as well as third party services such as Heroku, Dotcloud, Google AppEngine and more.

wercker

wercker

Wercker is a CI/CD developer automation platform designed for Microservices & Container Architecture.

GoCD

GoCD

GoCD is an open source continuous delivery server created by ThoughtWorks. GoCD offers business a first-class build and deployment engine for complete control and visibility.

Shippable

Shippable

Shippable is a SaaS platform that lets you easily add Continuous Integration/Deployment to your Github and BitBucket repositories. It is lightweight, super simple to setup, and runs your builds and tests faster than any other service.

Robot Framework

Robot Framework

It is a generic test automation framework for acceptance testing and acceptance test-driven development. It has easy-to-use tabular test data syntax and it utilizes the keyword-driven testing approach. Its testing capabilities can be extended by test libraries implemented either with Python or Java, and users can create new higher-level keywords from existing ones using the same syntax that is used for creating test cases.

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