Latest posts
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Common QA Pitfalls in AI-Driven Systems and Expert Fixes
AI-driven systems appear in robotics for perception and decision-making, in automotive for autonomous features, and in related fields for predictive maintenance or adaptive control. These systems introduce non-determinism, data dependencies, and emergent behaviors that challenge traditional QA. As of February 2026, common pitfalls stem from opaque models, insufficient testing coverage, and integration gaps, but structured…
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Ensuring Precision and Safety in Robotics – The QA Perspective
Robotics systems power applications from industrial manufacturing lines to collaborative robots in warehouses and advanced autonomous platforms in logistics or healthcare. Precision ensures repeatable, accurate movements and operations, while safety prevents harm to humans, equipment, or the environment. From a quality assurance viewpoint, achieving both demands structured processes that address mechanical reliability, software control, sensor…
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Safety in Autonomous Driving Systems – How to Achieve It
Autonomous driving systems (ADS) represent a transformative shift in mobility, with the potential to dramatically reduce traffic accidents caused by human error. According to established classifications, SAE J3016 defines six levels of driving automation. Level 0 involves no automation, with the human driver fully responsible. Level 1 provides driver assistance for either steering or acceleration/deceleration.…