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Artificial intelligence is everywhere these days! We hear about it all day long, in casual conversation, in the media, or at work. In the world of software testing, it's gradually becoming essential, not to replace QA teams, but to automate repetitive tasks and improve anomaly detection. Its main goal is to maintain a high level of reliability without lengthening development cycles. AI in software testing is a real breakthrough. How AI helps QA engineers Artificial intellige

Main Testing Approaches In a software project, not everything is tested the same way. Depending on the goal, the stage of the project, and the level of risk, you may choose a manual, automated, exploratory, static, or risk-based approach. Static testing makes it possible to detect defects without executing the code, for example through reviews or code analysis, while dynamic testing relies on running the software. The idea is not to choose just one method, but to combine the

In a context where product teams are shipping faster and faster, QA no longer has the luxury of being just a simple “safety net” at the end of the chain. ISTQB has clearly understood this and has evolved its Foundation syllabus towards a 4.0 version that is much closer to modern practices: Agile, DevOps, automation, continuous testing, and stronger collaboration with the rest of the team. Beyond a simple content update, this new version clarifies the tester’s role and aligns