Operations | Monitoring | ITSM | DevOps | Cloud

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.

We’ve recently completed a large beta test for our new product here at Testmo. We build a test management tool, so most of our users are professional software testers. As you can imagine, our customers are a rather critical group of users when it comes to software quality. We’ve learned some important lessons about running a large beta test and we want to share how we benefited from Sentry error reporting to identify, find, and fix issues quickly.