Systems | Development | Analytics | API | Testing

In the process of developing software, designing and performing testing is a critical aspect of ensuring high software reliability, improving software quality, and deploying strong fit and function. The shift-right testing approach moves testing to later in your production cycle as a way of doing this with more accurate user data and post-production testing practices. Also known as “testing in production,” with shift-right, you test software after it has been deployed.

In the process of developing software, designing and performing testing is a critical aspect of ensuring high software reliability, improving software quality, and deploying strong fit and function. The shift-right testing approach moves testing to later in your production cycle as a way of doing this with more accurate user data and post-production testing practices. Also known as “testing in production,” with shift-right, you test software after it has been deployed.

It’s impossible to learn about containerization without hearing about Docker and Kubernetes. These two tools together dominate the world of containers, both being the de facto standard in what they each do. When you’re first getting started learning about containers, it can be quite a challenge to figure out the differences between these two tools.

Today’s complex, dynamic applications demand rigorous resilience testing. A common hurdle is accurately mimicking real user behavior. This post discusses a possible solution: production traffic replication (PTR), a technique that captures actual user interactions to enhance chaos testing, and the principle of intentionally introducing failures to evaluate application recovery.

Kubernetes is an incredibly powerful solution, but testing the Kubernetes Ingress resources themselves can prove to be quite tricky. This can lead to significant frustration for developers – bugs can pop up in production that weren’t caught during testing, workflows that make sense on paper might fail in practice, and so forth.

Local preview environments are transforming how developers test and validate code changes before merging them into the main codebase. Acting as temporary cloud environments, they provide a production-like setting where new features and bug fixes can be tested in isolation, catching issues early and streamlining the development code review process. These environments are crucial for enhancing development velocity, especially in CI/CD workflows used by DevOps engineers and QA teams.

Today’s software testing trends show the growing demand for more efficient and automated API testing. Manual testing is not only time-intensive for internal testing teams, it can also lead to poor customer experiences. When manual testing processes cannot proactively discover issues, your customers may inevitably be the ones finding them. Many of the current test automation solutions today focus on the UI, while most API-level testing is still done manually.

One of the most important aspects of a product is the ability to showcase its functionality. In the API space, it’s very important for a product to not just claim something, but to show that utility and use. One of the best ways to do this is through the use of a demo environment. A demo environment is a perfect method to surface a specific use case or example for users, showcasing functionality that is often tailored to a specific interest group.

Today’s fast-paced development environments require new approaches to testing. Enter continuous performance testing. Unlike traditional performance testing, automated continuous performance testing is part of every build, monitoring the application continuously under increased load. With continuous performance tests, organizations with lots of users can expect to improve the customer experience, prevent major outages, and find and resolve issues faster.

Skaffold is a command line tool that facilitates continuous development by streamlining the workflow for building, pushing, and deploying Kubernetes applications. Skaffold handles the complexities of Kubernetes deployments, allowing developers to focus on local application iteration while managing deployments efficiently. Get started with our tutorial.