Container-native integration testing

Integration testing is still an important step in a CI/CD pipeline even when you are developing container-native applications. Integration tests tend to be very resource-intensive workloads that run for a limited time.

I wanted to explore how integration testing technologies and tools could leverage a container orchestrator (such as Red Hat OpenShift) to run faster and more-dynamic tests, while at the same time using resources more effectively.

In this post, you will learn how to build behavior-driven development (BDD) integration tests using Cucumber, Protractor, and Selenium and how to run them in OpenShift using Zalenium.

The code for the example of this article can be found on GitHub in redhat-cop/container-pipelinesh.

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Container Testing in OpenShift with Meta Test Family

Without proper testing, we should not ship any container. We should guarantee that a given service in a container works properly. Meta Test Family (MTF) was designed for this very purpose.

Containers can be tested as “standalone” containers and as “orchestrated” containers. Let’s look at how to test containers with the Red Hat OpenShift environment. This article describes how to do that and what actions are needed.

MTF is a minimalistic library built on the existing Avocado and behave testing frameworks, assisting developers in quickly enabling test automation and requirements. MTF adds basic support and abstraction for testing various module artifact types: RPM-based, Docker images, and more. For detailed information about the framework and how to use it check out the MTF documentation.

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An API Journey: From Idea to Deployment the Agile Way–Part III

This is part III of a three-part series describing a proposed approach for an agile API lifecycle: from ideation to production deployment. If you missed it or need a refresher, please take some time to read part I and part II.

This series is coauthored with Nicolas Massé, also a Red Hatter, and it is based on our own real-life experiences from our work with the Red Hat customers we’ve met.

In part II, we discovered how ACME Inc. is taking an agile API journey for its new Beer Catalog API deployment. ACME set up modern techniques for continuously testing its API implementation within the continuous integration/continuous delivery (CI/CD) pipeline. Let’s go now to securing the exposition.

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An API Journey: From Idea to Deployment the Agile Way–Part II

This is part II of a three-part series describing a proposed approach for an agile API lifecycle from ideation to production deployment. If you missed part 1 or need a refresher, please take some time to read part I.

This series is coauthored with Nicolas Massé, also a Red Hatter, and it is based on our own real-life experiences from our work with the Red Hat customers we’ve met.

In part I, we explored how ACME Inc. is taking an agile API journey for its new Beer Catalog API, and ACME completed the API ideation, contract design, and sampling stages. Let’s go now to mocking.

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An API Journey: From Idea to Deployment the Agile Way–Part I

The goal of this series of posts is to describe a proposed approach for an agile API delivery process. It will cover not only the development part but also the design, the tests, the delivery, and the management in production. You will learn how to use mocking to speed up development and break dependencies, use the contract-first approach for defining tests that will harden your implementation, protect the exposed API through a management gateway and, finally, secure deliveries using a CI/CD pipeline.

I coauthored this series with Nicolas Massé, who is also a Red Hatter. This series is based on our own real-life experience from our work with the Red Hat customers we’ve met, as well as from my previous position as SOA architect at a large insurance company. This series is a translation of a typical use case we run during workshops or events such as APIdays.

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JUnit 5 support lands in Eclipse Vert.x for testing asynchronous operations

JUnit 5 is a rewrite of the famous Java testing framework that brings new interesting features, including:

  • nested tests,
  • the ability to give a human-readable description of tests and test cases,
  • a modular extension mechanism that is more powerful than the JUnit 4 runner mechanism (@RunWith annotation),
  • conditional test execution,
  • parameterized tests, including from sources such as CSV data,
  • the support of Java 8 lambda expressions in the reworked built-in assertions API,
  • support for running tests previously written for JUnit 4.

Testing asynchronous operations is not straightforward

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Create a scalable REST API with Falcon and RHSCL

APIs are critical to automation, integration and developing cloud-native applications, and it’s vital they can be scaled to meet the demands of your user-base. In this article, we’ll create a database-backed REST API based on the Python Falcon framework using Red Hat Software Collections (RHSCL), test how it performs, and scale-out in response to a growing user-base.

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Manage test dependencies with Go

Introduction

I’m working on the upstream fabric8-wit project of openshift.io. In this Go project, we embrace testing as best as we can in order to deliver a stable component. Testing acts as our safety net to allow for fast-paced feature development. This blog post is about our recent change in our testing strategy. It is not as boring as it might sound at first. 😉

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