Maintain the reason behind each evaluation label

Record why the case matters

Each fixture should identify the task, expected behaviour and failure it is meant to catch. A short rationale can explain that an exception changes an approval decision, a unit changes a calculation or a target identifier controls data access.

Keep the authoritative source or business-rule reference with the label. If that source changes, maintainers need to know which cases may require review. A detached expected-answer string provides little help.

Distinguish regression cases from representative samples and challenge cases. Their purposes affect how results should be interpreted and whether a case can be retired.

Name the people who can change the standard

Engineering can maintain the harness, while subject-matter owners may need to decide whether an answer is correct. Identify an adjudication route for disagreements and a reviewer for consequential label changes.

Do not let a failing candidate automatically redefine the expectation. Require a reason when labels change, including whether the old label was wrong, the source changed or the product's scope changed.

Keep those revisions in history so earlier release reports remain explainable. A score is tied to the standard used at the time, not an eternal property of the model.

Transfer a complete comparison

Give the receiving team a baseline run, candidate run and a few changed cases. Ask them to explain the differences using the fixture rationale and traces. This tests whether the dataset can support a release decision without its original author present.

Include an evaluator disagreement and a stale source example. The team should know when to adjudicate, when to correct a fixture and when a result is inconclusive.

Document data access and retention rules for fixtures and reports. Evaluation ownership includes the copies of source material created during testing.

Keep the library aligned with real work

Assign a process for adding anonymised or synthetic reproductions of new failure mechanisms. Review coverage when capabilities, source collections or user tasks change.

Periodically inspect cases that no longer exercise distinct behaviour. Consolidate deliberate duplicates without removing difficult coverage merely to improve scores. The handover succeeds when the next team can explain why each important case exists and maintain a stable, honest standard as the application evolves.