Sikuli vs TagUI

Struggling to choose between Sikuli and TagUI? Both products offer unique advantages, making it a tough decision.

Sikuli is a Development solution with tags like gui-testing, image-recognition, automation.

It boasts features such as Image-based GUI automation, Cross-platform support (Windows, Mac, Linux), Support for major languages like Python, Java, JavaScript, Ruby, Image matching to identify and interact with GUI components, Recording and playback of user interactions, Visual debugging with screenshots, Integration with major test frameworks like JUnit and TestNG and pros including No need to deal with source code of application, Tests can be created using visual drag-and-drop, Tests are resilient to GUI changes, Simplifies test automation for graphical apps, Reusable image assets make tests robust, Support for multiple languages for test scripting.

On the other hand, TagUI is a Development product tagged with automation, testing, web, desktop.

Its standout features include Automates web testing using plain English scripts, Supports desktop automation for Windows applications, Integrates with CI/CD pipelines and tools like Jenkins, Open-source and available on GitHub, Cross-platform - works on Windows, Mac, Linux, Supports major browsers like Chrome, Firefox, Edge, API support for integration with other tools and languages, and it shines with pros like Easy to learn and use compared to traditional test automation, Plain English scripts are intuitive and readable, Open source and free to use, Cross-platform support, Integrates well with CI/CD workflows, Active community support.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Sikuli

Sikuli

Sikuli is an open source graphical user interface (GUI) automation and testing tool. It can identify and control GUI components by matching images of them, enabling test automation without needing access to the application's source code.

Categories:
gui-testing image-recognition automation

Sikuli Features

  1. Image-based GUI automation
  2. Cross-platform support (Windows, Mac, Linux)
  3. Support for major languages like Python, Java, JavaScript, Ruby
  4. Image matching to identify and interact with GUI components
  5. Recording and playback of user interactions
  6. Visual debugging with screenshots
  7. Integration with major test frameworks like JUnit and TestNG

Pricing

  • Open Source

Pros

No need to deal with source code of application

Tests can be created using visual drag-and-drop

Tests are resilient to GUI changes

Simplifies test automation for graphical apps

Reusable image assets make tests robust

Support for multiple languages for test scripting

Cons

Test maintenance overhead due to reliance on image assets

Brittle image matching can cause flaky tests

Limited built-in reporting capabilities

Steep learning curve for image-based testing

Not optimized for web or mobile app testing


TagUI

TagUI

TagUI is an open-source automation tool for testing web and desktop applications. It uses plain English scripts to automate repetitive tasks and simulate user interactions. Useful for regression testing and CI/CD pipelines.

Categories:
automation testing web desktop

TagUI Features

  1. Automates web testing using plain English scripts
  2. Supports desktop automation for Windows applications
  3. Integrates with CI/CD pipelines and tools like Jenkins
  4. Open-source and available on GitHub
  5. Cross-platform - works on Windows, Mac, Linux
  6. Supports major browsers like Chrome, Firefox, Edge
  7. API support for integration with other tools and languages

Pricing

  • Open Source
  • Free

Pros

Easy to learn and use compared to traditional test automation

Plain English scripts are intuitive and readable

Open source and free to use

Cross-platform support

Integrates well with CI/CD workflows

Active community support

Cons

Limited built-in reporting compared to commercial tools

Not designed for very large test suites

Documentation could be more extensive

Lacks some advanced features like object recognition