When it comes to building and deploying applications, one increasingly popular approach these days is to use microservices in Kubernetes. It provides an easy way to collaborate across organizational boundaries and is a great way to scale. However, it comes with many operational challenges. One big issue is that it’s difficult to test the microservices in real-life scenarios before letting production traffic reach them. But there are ways to get around it.

Oftentimes, people starting their journey in the field of software development don’t understand the importance of testing, including Golang app testing, until late in their careers. It’s essential to think about testing as an integral part of the software development lifecycle (SDLC) not only in theory but in practice, too. When building cutting-edge software, you need to make sure that the version being upgraded is error-free and that almost all of the failure cases have been considered.

When you think of software testing, what comes up first? For many developers, unit tests and integration tests are often top of mind. Both software testing methods are vital to writing and maintaining a high-quality production codebase. But they are not sufficient on their own. Your team’s testing practice should assess the entire application, observe the larger story of how it operates when functioning correctly, and raise alarms when deviations are found.

Testing is crucial in the software development phase. It helps ensure easy debugging, agile code, and enhanced reusability. Performing tests that cover all use cases helps prevent a codebase from breaking — minimizing exposure to vulnerabilities. Python has two main testing frameworks that developers can use, doctest and unittest.

As digital transformation accelerates and more organizations use software solutions to facilitate work operations, security threats have become more commonplace. Cybercriminals tirelessly develop ways to exploit software application vulnerabilities to target organizational networks. A notable example is the 2017 Equifax data breach, which exposed the personal details of 145 million Americans.

Confidence in the quality, robustness, and reliability of a product are among the most valuable qualities sought after by consumers as well as businesses. This confidence is built through the rigorous testing of a product. In software engineering, practices like extreme programming (XP) and test-driven development (TDD) champion the belief that automated testing should be used from the start of a project.

We’ve already outlined why API performance matters and what aspects of APIs to test, but what is the difference between API testing and monitoring? As with most things, context matters. The use cases for testing and monitoring are different because the objectives are different. The ultimate goal is to verify that your APIs are functioning properly, but staging environments vary significantly from production environments.

When a startup is in its very early stages, rapid iteration and dynamism are at the top of its priorities. The ability to do so, while maintaining a stable and high-quality product, is a big challenge facing the R&D group. We want to release features as quickly as possible, but this rapid velocity cane cause conflicts when writing in-depth, comprehensive tests.