APPLIED EVALUATION / OCTOBER 2026

When “Passed”
wasn't proof.

What evidence is enough to call something finished? In one AI-assisted software project, a validation process could report success without actually completing the check. I tightened the standard for accepting the result.

PRODUCT REQUIREMENTSQA JUDGMENTACCEPTANCE DECISIONS

CONTROLLED COMPARISON / SAME FIXTURES

A pass had to
earn its own proof.

Eleven scenarios ran three times per version, producing 66 controlled executions. The comparison measured acceptance decisions & process cleanup, not production customer outcomes.

HISTORICAL WRAPPER24/33

Correct decisions

9 false approvals · 3 fixture child processes left running
→
REVISED WRAPPER33/33

Correct decisions

0 false approvals · 0 fixture child processes left running

A controlled rerun, not an estimate. 11 scenarios × 3 repetitions × 2 versions = 66 executions.

01 / THE FAILURE

Success was being inferred from silence.

A code-checking wrapper could time out without printing compiler errors & still return a success result. The process hadn't finished, but the check could still get a pass.

Simply increasing the timeout wouldn't fix the faulty decision rule. Acceptance needed positive evidence that the process completed successfully.

01

Silent timeout

No diagnostics appeared before the time limit expired.

02

Ignored termination

The timed-out process did not stop when initially requested.

03

Late success code

A zero exit code arrived only during termination, not completed validation.

02 / THE CORRECTION

Block failure.
Block uncertainty.

Under the revised decision rule, a pass requires completed success. A failed, interrupted or inconclusive check must block acceptance rather than inherit the appearance of success.

The stricter gate subsequently exposed accumulated type errors that needed correction before a later change could be accepted.

03 / A REUSABLE METHOD

Prove the job.
Not just the component.

My contribution was defining product requirements, expected behavior, the QA judgment & acceptance decisions, then directing AI-assisted implementation & fresh verification.

01

Define the outcome

Who acts, what changes & what should happen when an action fails.

02

Match evidence to the claim

Use the right kind of test for structure, logic, workflow & completion.

03

Test failure too

Include interruptions, retries & denied paths. Missing evidence is inconclusive.

04

Record the decision

Connect expected & actual results to a version, evidence & a next step.

View the test record

In the October 5 controlled comparison, source & test-file hashes were verified. The harness ran process fixtures for success, failure, timeout & termination in separate local trial directories.

  • 66 controlled process runs
  • 22 unchanged repository regression tests passed
  • 10 new request-helper probes passed
  • Process cleanup checked before fixture cleanup

These results establish outcomes for this specific controlled evaluation. They do not represent a general production reliability rate.

AI can accelerate the work.
It doesn't get to lower the standard
for calling it done.

See the systems I build ↗