This project serves as an example for writing Automation(Selenium, Rest Api and Web-socket) using Gauge Installation Help Documentation - https://docs.gauge.org/getting_started/installing-gauge.html?os=windows&language=python&ide=vscode

Pre-requisites (Install the following softwares) -

pip install getgauge==0.3.17 –user
pip3 install --upgrade protobuf==3.20.0
pip install selenium
pip3 install websocket-client
python -m pip install requests
pip install webdriver-manager<br>

To generate reports -

gauge install html-report
gauge install json-report
gauge install xml-report
gauge install spectacle
gauge install flash

To generate spectacle report -

gauge docs spectacle

Run specific features with environment files –

gauge run --env="default" specs

Run specific feature file –

gauge run .\specs\login_csv.spec

Suppose that you have a specification file, called let say Login.spec, and you want to list all the scenarios or tags. Below command can be used :

gauge list --tags specs/login.spec
gauge list --scenarios specs/login.spec

Gauge Tests Dockerization -

This is an example project that demonstrates how to Dockerize a Gauge project with Python and Requests.

Prerequisites:

To run this project, you will need to Docker installed on your machine.

Building the Docker Image -

To build the Docker image for this project, run the following command:

docker build -t gauge-tests .

This command will create a Docker image with the name gauge-tests based on the instructions in the Dockerfile in the root directory of the project.

Running the Tests To run all the specs in the project, use the following command:

docker run --rm -v <project root directory>:/app gauge-tests gauge run app/demo/features/specs
Example:
docker run --rm -v C:\Project\Python\gauge-tests:/app gauge-tests gauge run app/demo/features/specs

This command will start a Docker container with the gauge-tests image and run the Gauge tests in the app/demo/features/specs directory inside the container.

To run a specific spec file, use the following command

docker run --rm -v C:\Project\Python\gauge-tests:/app -w /app/demo gauge-tests gauge run features/specs/rest_api.spec

This command will start a Docker container with the gauge-tests image, set the working directory to app/demo, and run the rest_api.spec file inside the container.

Conclusion

With Docker, you can easily package and run Gauge projects in a consistent and isolated environment. By following the steps in this README, you should be able to Dockerize your own Gauge projects and run them anywhere with minimal setup.