AI can generate code quickly. That does not mean behavior is correct, complete, or safe to evolve.
GOV-05 treats testing as delivery evidence, not ceremony.
Testing perspective (summary)
From a testing perspective, the job is simple:
- prove intended behavior actually works,
- expose where behavior breaks,
- prevent regressions as changes continue.
If tests cannot prove the claim, the claim is not done.
Why this matters in AI-assisted delivery
AI can produce plausible implementation faster than teams can reason about edge cases.
Without strong testing perspective, teams get:
- "looks right" merges with hidden defects
- overconfidence from shallow or irrelevant test passes
- repeated regressions in high-change areas
- weak release confidence despite high activity
What good testing evidence looks like
A useful test strategy should provide clear evidence for:
- success paths (expected user/system outcomes)
- failure paths (validation, error handling, guardrails)
- high-risk edges (state transitions, race conditions, boundary inputs)
- regression stability (behavior remains correct after future changes)
Test-to-intent rule
Testing must map back to intent.
For each meaningful behavior, you should be able to answer:
- Which requirement does this test prove?
- Which acceptance criteria are covered?
- What failure would this catch if behavior drifts?
If those answers are unclear, test coverage is likely cosmetic.
Practical execution standard
Use testing as a layered evidence model:
- unit: logic correctness
- integration: contract and boundary behavior
- end-to-end: user-critical workflows
Not every change needs every layer, but critical paths must have sufficient proof.
Common anti-patterns to avoid
- passing tests that do not validate actual requirements
- broad snapshots with no behavior intent
- flaky tests normalized as acceptable
- reporting completion without direct evidence links
Bottom line
In GOV-05, tests are not a checkbox. They are the proof system for delivery claims.
When testing perspective is strong, velocity stays high without sacrificing reliability.
Read the canonical page: