Three related approaches
| Approach | Strongest use | Typical limit |
|---|---|---|
| Automated scanning | Known patterns, formatting, dependency alerts and repeatable static checks. | May not understand the intended business or system behaviour. |
| Human code review | Design reasoning, maintainability and implementation quality within team context. | Reviewers can share the author's assumptions and may not reproduce runtime behaviour. |
| Independent verification | Testing a specific claim or behaviour against evidence and agreed acceptance conditions. | A bounded result applies only to the scope and conditions examined. |
Automated review is a filter
Static analysis, linters, dependency scanners and AI review assistants can examine large amounts of code quickly. They are excellent at applying known rules consistently. Their output still needs interpretation: a warning may be irrelevant, and a clean scan may say little about whether the system performs the intended workflow.
Verification is tied to a decision
A verification check begins with an expected outcome. The investigator selects conditions and observations that can support or contradict that outcome, then preserves the evidence. This makes it useful for release blockers, regressions, integrations and customer acceptance questions.
Use them together
A sensible sequence is to run automated checks continuously, use human review for design and implementation, and add independent verification where one uncertain behaviour materially affects the decision to release, accept or invest further effort.
Choosing the smallest useful check
If the question is “does this code contain a known unsafe pattern?”, a scanner may be enough. If it is “does this fix prevent the reported failure without breaking the adjacent workflow?”, reproduce the behaviour and preserve the result. If the consequences reach safety, security or regulation, use the relevant specialist assurance process.