AdaCore Pushes AI-Generated Code Into Admission Control
AdaCore’s approach shows how AI-generated code can be stopped at the gate unless it comes with proof, tests, coverage, and traceability.
What is this trend?
AdaCore is treating AI-generated code as a gated quality-control problem, requiring proof, tests, coverage, and traceability before merge.
- AI output must earn entry through formal quality gates, not just human review.
- Proof, requirements-linked tests, and coverage become acceptance criteria.
- Traceability shifts from documentation to a blocking control.
- QA teams need machine-checkable evidence for every AI-assisted change.
What’s the latest?
AdaCore’s GNAT Foundry demonstrator is the next step in the story: it applies formal proof, requirements-based testing, structural coverage analysis, and traceability checks to AI-generated code changes before they are a
How it developed
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