TEST THE RISK!

Postman seems to become a defacto tool for service testing because the Postman is very user-friendly, easy-to-learn, all-in-one, lightweight and collaborating tool. Postman has been used for a long time but recently it has growing popularity because of a stable native application, collaboration feature after version 6.2, sharing of collections for team, interactive working with the team, mocks for isolated testing, environments for running the test for different test environments such as local, development, stage ... and many more features. For me, one of the biggest features is easy-to-use for everyone in a team so everyone in a team can use and update a postman collection easily. In this post, I want to explain how postman can be used efficiently. Testing a Service and Writing Tests With postman testing service is simple. Postman supports many methods like POST , GET , PUT , PATCH . Just select the correct method and hit the service URL you want to test. Postman also has everyth

If you are using Capybara with Cucumber, tagging is a very efficient feature of the Cucumber for manageable test cases. Tagging can be also used for managing test environments, like @test may mean that these tests run only in the test environment so set the environment as the test for me. Likely @live may mean that these tests can run on the live environment, assume that you are applying continuous testing. You can also give the scenarios for device-specific tags, like @ihphone6_v may say these tests are for iphone6 with the vertical mode. Moreover, with tagging, you can also make an isolation strategy for running your test in parallel. Each parallel suits should have its own data like users, active records, address, credit card info, and ext. I have used the tagging for running tests in dockers. In this post, you can find some practical way of running Capybara with Cucumber in Dockers. Creating Docker Image: Dockerfile I am using Ruby version 2.3 so in Dockerfile gett

I have shared some posts how to run Locust in Local or in Cloud, as slave or master. At this time I want to share how you can run it in a Docker. To fully get the benefits of Locust I am using it with Python 3 so I created a Docker file and images upload to the Docker Hub , and the project is on the GitHub .  The Dockerfile has the minimum available requirements but we also have a new file called `requirements.txt` which you can add required python libraries to install inside the container by pip install lib . The Dockerfile is: When you got the docker-locust image, you can now run your script. At the end of the Dockerfile you can see that ENTRYPOINT [ "/usr/local/bin/locust" ] this enable us to use the image as service  which means that you can directly call it same as using Locust installed locally. See the run command below: Running Locust in a Docker Running the Locust with my image is pretty simple if you used without docker, just try other options w