A simple rule of thumb: In general, AI is best reserved for well-defined, repetitive tasks. This includes anything that ...

Revolutionary autonomous testing agent catches 847 bugs monthly while reducing QA cycle time from weeks to daysQA flow, ...

As AI adoption accelerates across industries, untested software is becoming a silent risk, enabling large-scale failures in ...

Understand why testing must evolve beyond deterministic checks to assess fairness, accountability, resilience and ...

AI is transforming the software landscape, with many organizations integrating AI-driven workflows directly into their ...

G2’s Winter 2026 report highlights growing adoption of AI-driven automation testing tools, reflecting how user feedback is shaping enterprise QA and testing decisions.

Healthcare and finance apps operate in two of the most demanding digital environments today. Both industries deal with ...

What if the very process meant to ensure your AI applications work flawlessly is actually holding you back? Manual testing, once the backbone of quality assurance, is now a bottleneck in the ...

From generating test cases and transforming test data to accelerating planning and improving developer communication, AI is having a profound impact on software testing. The integration of artificial ...

Testing APIs and applications was challenging in the early devops days. As teams sought to advance their CI/CD pipelines and support continuous deployment, test automation platforms gained popularity, ...

As AI technology and no-code automation tools continue to evolve, manual testing seems to be losing its edge. This perception may or may not align with reality, as multiple factors are impacting ...