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Testers also may be technically unaware of how to even get started testing an API, so they simply focus on what they know-which is UI automation. Testers believe developers should be doing API testing, while developers believe the opposite.
It still hasn't taken off, though, for several reasons.įor one, even though many organizations claim to be agile, many still have distinct roles defined for developers and testers. Thinking that API testing was about to take off, I wrote a book about it back in 2014. With microservices making up the backbone of most newer development efforts, API testing becomes even more critical than before. Focusing solely on UI automation-which is notoriously slow-can kill your test automation efforts.Īs you scramble to ensure that your applications are ready to ship, API testing should be part of your overall automation strategy.ĪPIs are the basis of modern software development, especially as more and more teams move away from monolithic applications and begin adopting a microservices approach to software development. Why perform API testing?Īs more companies make the shift left toward DevOps, continuous integration (CI), and continuous deployment (CD), test feedback needs to be quicker than ever. While most vendors are talking up the benefits of AI- and UI-based testing tools in general, AI- and machine learning-based applications that help with API testing have arrived.īefore you begin API testing, however, make sure you understand test automation basics and know how to avoid the most common test automation mistakes.
How do you find the right open-source API testing tool for your needs? Since my last roundup of the best candidates, a few more tools have appeared that warrant consideration-and there's a new technique that's all the buzz in AI automation circles that you need to know about.
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