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Usually, no. A golden-file failure after a model change is a signal to investigate, not a reason to replace every expected output. Regenerate only the affected snapshots, inspect the diff, and accept a new baseline only when the changed behavior is intentional. Keep that work separate from curating a versioned evaluation set used to compare models over time.
What a golden-file failure tells you
A golden file stores an expected output so a later run can be compared against it. A mismatch shows that the output changed; it does not, by itself, establish that the change is a bug or that it is correct. Snapshot testing detects differences, while a person or a separate test must judge whether those differences meet the intended behavior. The Go Golden library documentation describes snapshot testing and a human approval mode.
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Should you update golden files after changing models?
Update only the expectations affected by the change, and only after reviewing them. A model change may alter generated text or other outputs, but bulk replacement can conceal unrelated regressions, unstable fields, or missing test coverage. TensorFlow Federated’s golden-testing guidance recommends checking for unanticipated changes in the resulting diff.
Think of regeneration as writing a candidate baseline—not approving it. Some tools make that distinction explicit: the Go Golden library documents an approval mode in which a test remains failing until a person accepts the snapshot.
A safer workflow for refreshing snapshots
- Identify the changed behavior. Find which tests cover it and determine whether the mismatch is plausibly caused by the model update. A failing snapshot alone does not answer whether the new result is acceptable.
- Regenerate narrowly. Use the project’s documented update mechanism and limit its scope to the relevant test or package when possible. SCION documents package-level and repository-wide update examples in its Golden Files guide; TensorFlow Federated documents an update argument for expected files. These are project-specific approaches, not a universal command or flag.
- Review the diff. Check whether each changed output is expected, whether unrelated outputs moved, whether unstable fields are driving noise, and whether user-visible requirements still hold. TensorFlow Federated specifically advises checking for unanticipated changes.
- Approve deliberately. Accept the new baseline only after that review. If the tool supports human approval, use it to keep regeneration separate from acceptance.
- Handle nondeterminism explicitly. If outputs vary between runs, do not treat every variation as a routine snapshot refresh. SCION documents a separate update flag for nondeterministic golden files; that is an example of a project-specific policy, not a standard shared by all test frameworks.
Golden snapshots are not the same as a model evaluation set
A snapshot generally captures an expected output for a particular test case. A curated evaluation set is a versioned collection of inputs and expected outcomes used as a stable reference when comparing model behavior. Replacing snapshots after a model update does not automatically justify changing that reference set: doing so can make comparisons across model versions less meaningful.
Golden-Eval’s methodology describes freezing a specific version as the reference for an evaluation campaign. Keep the evaluation inputs and labels fixed for a comparison, and version changes when there is a reason to revise the set. New evidence, feature changes, incidents, or adversarial testing can justify adding or changing cases; a baseline update triggered by a failing test is not, on its own, such a reason.
Keep model training separate from regression checks
Model testing has concerns that ordinary output snapshots do not cover. Google Research’s ML Test Score publication cautions against golden tests that partially train a model. Keep training and regression evaluation conceptually distinct so that a test intended to detect a behavior change does not also change the model being evaluated.
Choose the right update and review scope
| Approach | Update scope | Approval control | Best fit |
|---|---|---|---|
| Targeted snapshot refresh | One relevant test or package; SCION documents package-level updates. | Review and accept the changed snapshot; Go Golden documents a human approval mode. | A known behavior change in a limited set of outputs. |
| Whole-suite snapshot refresh | Repository-wide; SCION documents a repository-wide update example. | Requires careful review of the wider diff before accepting. | Only when the intended change genuinely affects the suite broadly. |
| Nondeterministic-output workflow | Project-specific; SCION documents a separate update flag for nondeterministic golden files. | Use the project’s explicit policy rather than treating variation as an ordinary deterministic snapshot. | Outputs that cannot be expected to remain identical between runs. |
| Versioned evaluation set | A fixed, named set of inputs and expected outcomes for an evaluation campaign. | Change and version the set deliberately; Golden-Eval describes freezing a reference version. | Comparing model behavior against a stable reference over time. |
The mechanisms above are documented by their respective projects; flags, commands, and approval behavior are not interchangeable across tools.
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