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.
Testing isn’t simply about catching bugs anymore. It’s about delivering reliable experiences at the same pace that globally distributed developers merge code, spin up containers, and release microservices.
Complexity is a property of software systems with lots of components that interact in lots of ways. Bugs thrive in complexity. Inert bugs in one component may be activated by seemingly unrelated changes in another. Latent bugs may take years to be observed or detected by software testing.
When it outsources software QA, management looks only to cut costs. What it doesn’t understand is the loss of product knowledge that comes with it. In this article, Ann-Sofie Ollikainen explains why test coverage metrics alone cannot replace long-term product understanding.
In this article, Tanvi Mittal explains how to exploit production logs to detect issues that were not covered by pre-release test suites.
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