Continuous Testing in the Age of AI

Testing and the CI/CD Process

In the post, Continuous Testing: Required But Not Enough, our CTO Kevin Surace explored the necessity of Continuous Testing, but also its shortcomings. As he covered there, continuous testing requires a range of automated testing approaches, covering unit tests, API and integration tests, as well as more complete end-to-end tests. And it must include both functional and non-functional tests. There’s full agreement in the industry that this type of continuous testing program and approach to the SDLC is a must.

Kevin also noted that AI can strengthen your continuous testing by enabling automatic regression testing. AI writes the tests based on your actual user behavior, and because the process is fast, it can be done continuously.

But are there other ways that AI can strengthen your continuous testing efforts? Absolutely!

Test Creation & Maintenance: Key Pain Points for Continuous Testing

The goal of continuous testing is to make testing automatic and to spur its execution rapidly within the DevOps process. But creating and maintaining test automation has been anything but quick and automatic. In recent years, we have seen AI reduce the burden of test maintenance, but outside of the regression testing arena, the test creation process has still been slow, even with the proliferation of low-code tools, and still relatively labor intensive. These complications have resulted in reduced coverage during the continuous testing process.

Here’s where generative AI, like our AI Blueprinting capability, can help. With blueprinting, AI can follow all the possible paths through your application, writing and executing the tests as it goes. This process can be initiated automatically–perfect for continuous testing–and can be tuned for completion in about an hour. This means that you can achieve near-complete Application Coverage in every DevOps cycle.

AI-powered Continuous Testing Delivers More

While a key goal of continuous testing is to identify defects earlier in the SDLC process, another critically important objective is risk reduction. As more and more businesses depend on the quality of their applications to adequately and successfully serve their customers, releasing a new build without adequate risk assessment is unacceptable. And risk assessment should go beyond the identification of individual defects or performance issues; risk should be evaluated holistically for the best results possible.

This is another area where AI, and more specifically AI Blueprinting, can deliver more value for teams. The comprehensive Coverage Map generated during blueprinting provides more than a simple report of bugs and errors found. The map enables teams to see how their application is performing as a whole and identify areas or groups of issues visually. Teams can now make go/no-go decisions on whether to release not just based on defect identification, but also on a more intuitive graphical representation of overall performance. AI provides this extra confidence that wasn’t possible before.

If your team is ready to benefit from all that AI offers and apply truly effective continuous testing to your software delivery pipeline, now is the time to reach out and request a meeting with our team.  We would be happy to show you how AIQ and its advanced generative AI can reimagine your test suite and increase the productivity of your QA team.

And if you want to learn more about AI-driven autonomous and continuous testing, download our eBook here.

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