CT-AI: Certified Tester AI Testing Version 2.0

Machine learning models require large volumes of relevant, well-labeled historical data to learn accurate predictive patterns. An old application whose defect records are explicitly linked to failed tests and production incidents provides exactly this: a rich dataset where each defect's real-world impact (i.e., its true criticality, as evidenced by test failures and production incidents) is known. This direct traceability between defect characteristics and actual consequences gives the ML model clear, reliable labels to learn from, enabling it to accurately classify the criticality of newly reported defects. In contrast, a new application lacks sufficient historical data, a new team (despite existing defect data) does not affect the model's data-driven learning process as directly as label quality does, and a dataset skewed heavily toward non-critical defects suffers from class imbalance, which degrades the model's ability to correctly identify critical defects.