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Test classes that were once simple now require extensive setup and complex assertions. Small changes in automation break multiple tests. Maintaining coverage feels increasingly expensive. I want to understand why this happens and how teams manage it.
I trained an object detection model on a mixed dataset containing people, vehicles, and small objects like phones and traffic signs.The model detects large objects such as cars and people very reliably.However, it almost completely ignores smaller objects, ...
Tests pass, but production fails. I want to understand why
