AI-generated code remains engineering work that must be reviewed, tested and governed. Teams need to understand why the code exists, how it relates to requirements and what evidence supports its acceptance, just as they do for human-written changes.
Why this matters
Generated code can accelerate drafting, but speed does not confirm correctness, security or fit with product intent. Established review, testing and release controls remain central to dependable delivery.
A practical approach
- Keep the originating work item or requirement linked to the change.
A practical example
Review generated code for correctness, security, licensing and maintainability under organizational policy.
What to keep in mind
Run relevant automated and human tests, including regression checks.
Frequently asked questions
Record who reviewed and accepted the change and what evidence was considered.
Monitor the result after release and address defects through the normal lifecycle.
Further reading
The views in this article are my own. Product capabilities and availability can change; consult current vendor documentation and your organization’s requirements.
Newsletter
Imran's Insights
Perspectives to help your team navigate AI, software delivery, engineering and high-performance computing.
