There are many cases where AI models that worked fine in development start to behave badly in production. In this article, Kanika Vatsyayan discusses the causes that lead to this situation. He recommends moving from static assessments to continuous quality engineering for AI models.
When you add new code to your applications, it is associated to the test scripts that should validate it. Do you give enough importance to these tests? In this article, Eli Lopian explains that when pull requests are opened, you should review the tests scripts as much as the code.
Software testing efforts are mainly focused on the application code itself. However, recent major failures were due to configuration changes or system data. In this article, Sergei Cherednichenko discusses how quality assurance efforts should widen their scope from an application-centric perspective to a system-wide reliability verification.
AI-assisted code generation has changed the capabilities of organizations to produce code. In this article, Hélder Ferreira discusses the impact on software quality created by this new situation. The main problem is that this new code is often created by tools that lack the knowledge of the global context of both the application infrastructure and the process being automated.
Today, you can find tools that generate code and AI-assisted test automation tools that will handle the quality assurance part. Is it the death of traditional manual testing? In this article, Rimpal Mistry reminds us that human validation is still needed to provide the final check of delivered software.
AI-written code can sail straight through your CI pipeline — lint clean, tests green, coverage above threshold — and still ship a bug nobody thought to test for. This article breaks down why AI code passes Continuous Integration (CI) checks built for a different generation of bugs, and lays out a short list of concrete, budget-free gates that catch what your CI pipeline can’t see today.
In 2026, the U.S. B2B technology market clearly illustrates a challenge faced by many growing startups: even a technically strong product may not be ready to meet the requirements of large enterprise customers.
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