Study finds AI-generated code flags zero true error-swallowing cases across 120 samples

A developer ran a controlled experiment generating 120 code samples using Qwen2.5-Coder 1.5B across 12 tasks in Python and TypeScript to test whether small AI models routinely hide or suppress errors. A Semgrep-based static detector flagged four candidates as potential error-swallowers, but manual review confirmed all four were either false positives or legitimate, documented fallbacks. The experiment found that Python generations mostly let exceptions propagate or raised them explicitly, while TypeScript samples favored try/catch blocks with logging or re-throwing. The researcher's key conclusion is that whether code truly swallows an error cannot be determined from syntax alone, as the same pattern can represent either a sound design contract or a genuine hazard depending on context. The study acknowledges significant limitations, including a single small model family, only 12 tasks, and sole-rater adjudication, and makes no broader claims about AI code versus human-written code.