Gauge icon
Gauge icon

Gauge

Gauge is a free and open source test automation framework that takes the pain out of acceptance testing.

Gauge screenshot 1

Cost / License

  • Free
  • Open Source

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Platforms

  • Mac
  • Windows
  • Linux
  • FreeBSD
  • Red Hat
  • npm
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  1.  Web Testing

 Tags

  • development
  • testing

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Gauge information

  • Developed by

    US flagGauge
  • Licensing

    Open Source (Apache-2.0) and Free product.
  • Written in

  • Alternatives

    1 alternatives listed
  • Supported Languages

    • English

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Development

GitHub repository

  •  3,122 Stars
  •  346 Forks
  •  73 Open Issues
  •   Updated  
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Gauge was added to AlternativeTo by RemovedUser on and this page was last updated .
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What is Gauge?

Create readable and maintainable tests Gauge tests are in Markdown which makes writing and maintaining tests easier. Reuse specifications and robust refactoring to reduce duplication. Less code and readable specifications means less time spent on maintaining the test suite.

Work with languages and tools of your choice Gauge works with multiple languages, IDEs, CI/CD tools and automation drivers. You don't have to learn a new language or tool to get your test automation tool to work for you.

Extend with plugins Gauge has a robust plugin architecture and plugin ecosystem. You can easily extend Gauge to add support for IDEs, drivers, datasources, text execution events or your favourite programming language.

Quickly detect issues Don’t waste time going through stacktraces. Gauge takes a screenshot on a test failure allowing you to get a visible picture of what went wrong. Reports are available across multiple formats (XML, JSON, HTML).

Scale your tests Save time on tedious setup for parallelization of tests on your CI/CD pipelines. With Gauge's out of the box parallelization support, speed up your tests and optimally utilize your infrastructure.

Manage your data sets better Gauge supports data-driven testing. Keep your data sets out of your test scripts to run them repeatedly against multiple data sets. Easily test with large data sets, while keeping specifications highly readable.

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