Welcome to the world of Zebrunner Reporting! This powerful tool is designed to simplify your test automation management, making it easier to track results, spot bugs, and optimize your testing processes. In this article, we’ll walk you through the installation process, core features, and some troubleshooting tips if you encounter any issues along the way.

What is Zebrunner Reporting?

Zebrunner Reporting is a comprehensive test automation management tool that accumulates and illustrates test results clearly and transparently. It provides detailed reports complete with logs, screenshots, and video recordings of your test sessions. By using Zebrunner, you can reduce the workload for automation teams and facilitate early bug detection and resolution during the release cycle.

Getting Started

Prerequisites

Before diving into installation, you need to ensure your system meets the following hardware requirements:

  • Operating System: Linux Ubuntu 16.04, 18.04, 20.04 or Linux CentOS 7+, Amazon Linux 2
  • CPU: 4+ Cores
  • Memory: 16 Gb RAM
  • Free Space: SSD with 64 Gb+ of free space

Installation Steps

Follow these simple steps to get Zebrunner Reporting up and running:

  1. Install Docker Engine and Docker Compose.
  2. Clone this repository recursively and launch the setup process:
    git clone --recurse-submodule https://github.com/zebrunner/reporting.git
    cd reporting
    .zebrunner.sh setup
  3. Start the services:
    .zebrunner.sh start
  4. Open http://hostname:port or https://hostname:port to access the interface, and login using admin/changeit credentials.

Follow the installation and configuration guide in Zebrunner CE to leverage Reporting components effectively for Test Automation.

Core Features of Zebrunner

  • Real-time test results tracking via websockets
  • VNC streaming and video recording of test sessions
  • Customizable widgets and dashboards
  • Robust user management with authorization policies
  • Integrations with JIRA, TestRail, and Slack
  • Ability to compose automation reports and send them via email

Understanding the Reporting Structure

Imagine Zebrunner as the conductor of an orchestra, ensuring that every musician (test case) is playing in harmony. The various components of Zebrunner, such as the reporting-service and reporting-ui, work together to compile a beautiful symphony of test results, showcasing how well your automation is performing.

Troubleshooting Tips

If you encounter any issues during installation or usage, here are some troubleshooting ideas:

  • Ensure that Docker is installed correctly and running.
  • Verify that you have the correct permissions to execute the setup scripts.
  • Check your system’s resource availability to avoid slow performance.
  • If things are not running as expected, try restarting the services using the command:
    .zebrunner.sh restart

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With Zebrunner Reporting, streamlining your test automation processes becomes an enjoyable experience. It allows for real-time insights, so you can focus on what really matters: delivering quality software to your users. Enjoy leveraging Zebrunner’s powerful reporting capabilities in your testing workflow!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

About the Author

Hemen Ashodia

Hemen Ashodia

Hemen has over 14+ years in data science, contributing to hundreds of ML projects. Hemen is founder of haveto.com and fxis.ai, which has been doing data science since 2015. He has worked with notable companies like Bitcoin.com, Tala, Johnson & Johnson, and AB InBev. He possesses hard-to-find expertise in artificial neural networks, deep learning, reinforcement learning, and generative adversarial networks. Proven track record of leading projects and teams for Fortune 500 companies and startups, delivering innovative and scalable solutions. Hemen has also worked for cruxbot that was later acquired by Intel, mainly for their machine learning development.

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