Artificial Intelligence (AI)

Software testing has evolved dramatically over the past decade. Applications are now deployed continuously, Agile delivery cycles are shorter than ever, cloud-native architectures are becoming standard, and organizations are under constant pressure to deliver faster while maintaining high quality. At the same time, automation has become one of the most important pillars of modern software

Applications developed by Artificial Intelligence are fundamentally changing software engineering. In these systems, AI is not an auxiliary feature or a plugin. It is the core decision-making engine that defines application behavior. This creates a structural shift in quality assurance. Traditional QA assumes deterministic logic, stable outputs, and rule-based validation. AI-developed applications violate all these

Test automation at scale is not a tooling problem. It is an architecture problem. During Tech Talk #16, we explored how modern QA organizations can move from fragile test scripts to enterprise-grade automation platforms by combining: This article provides a practical and deeply structured blueprint to design, build, and scale such systems. 1. The Reality

Why Most AI QA Initiatives Fail Many organizations successfully experiment with AI in QA but fail to scale it to production. The reason is not a lack of capability, but a lack of reliability. AI systems that perform well in controlled environments often break down in real-world conditions. This is because production environments introduce variability,

Artificial intelligence has become a core component of modern software engineering. However, in the field of test automation, its real impact is still underestimated. Today, two major tools dominate the discussion: While Copilot is widely known for accelerating code completion, Claude Code introduces a different paradigm focused on reasoning, architecture, and system-level understanding. But which