Regression Test Local Model Output

Direct answer

A model-output regression suite should answer whether a defined product task still meets its acceptance rules after a change. It is not automatically a benchmark of general language quality. Pin the task corpus, prompt template, model artifact, runtime, decoding configuration, app build, and expected properties. Keep deterministic checks for structure, required fields, protected terms, and safety boundaries; use human review for semantic properties that cannot be reduced to exact string matching. Report failures and fallback, not only the best sample. Generation can vary with sampling, backend, runtime revision, and hardware path. A test should specify what is expected to be exact and what is evaluated by a bounded rule. For a JSON task, validate schema and domain values. For translation, check required meaning units and forbidden omissions using an authored rubric; do not claim measured translation accuracy unless a versioned corpus and scoring method exist. For a tool-calling task, validate tool name, arguments, authorization, and host-side result separately from the natural-language response. Maintain fixtures that represent the product’s actual inputs: short and long text, punctuation, ambiguous requests, supported and unsupported language paths, empty input, malformed source, and prior user edits. Avoid storing private customer material; create synthetic or consented, redacted fixtures. Each fixture needs provenance, expected behavior, and an explanation of which failure it catches. Do not generate synthetic expected labels by asking the same model to grade itself without independent review. Run a baseline before modifying the prompt or runtime. Change one factor at a time where feasible; otherwise record the full changed configuration. Compare structured outputs, user-visible status, errors, fallback, latency only if measured under a documented protocol, and memory only if captured by an appropriate tool. A text output snapshot alone misses regressions in cancellation, persistence, UI states, and privacy. Store raw run records with timestamp, artifact and runtime identities, test fixture revision, sampling configuration, device when relevant, result, failure stage, and reviewer decision. Keep input text only when it is non-sensitive or explicitly approved for the test set. Publish a method before summary values. If no raw records exist, publish the procedure and acceptance criteria, not numerical results. A small regression suite is useful when every case is tied to a user-visible requirement and each failure can change a release decision. Add a fixture after a real regression, a supported official behavior change, or a documented product requirement. Remove or merge redundant cases when they test the same intent. Workflow: 1. Write the product task, user impact, and acceptance properties before selecting a model output example. 2. Version fixtures, prompts, artifact, runtime, app build, and decoding settings; record provenance and privacy classification. 3. Run deterministic structure and boundary checks, then conduct human evaluation for semantics that lack an objective exact match. 4. Preserve all failures, fallback, and incomplete cases; do not average away a critical task failure or cherry-pick one fluent response. 5. Compare the new run with the pinned baseline and state limits; publish numeric claims only when raw data and validation rules exist. This approach tests a product behavior. It does not rank models, certify a phone, or prove a universal accuracy level.

A prompt edit changes a local translation workflow

A team updates a prompt to improve a travel phrase translation. One example looks more fluent, but there is no record of whether a required negation disappeared, the selected language direction changed, or cancellation and error states still work.

Decision

Use a versioned, privacy-safe fixture set with separate structure, task-semantic, and lifecycle checks; do not judge the change from one attractive response.

Expected outcome

A reviewer can trace each regression decision to a requirement, fixture, configuration, and preserved run result.

Evidence to collect

The claim becomes reviewable only when the following evidence is attached to the same artifact and test run:

• Versioned fixtures
• Property and format assertions
• Change attribution

A release claim is valid only for the recorded configuration and acceptance set. Generalization requires additional records.

Implementation workflow

1. Define acceptance properties first. Specify required output structure, meaning units, forbidden omissions, safe refusal behavior, and UI outcomes. State which checks are exact and which need a human rubric.
2. Pin test inputs and configuration. Version the synthetic or redacted fixture, prompt, artifact revision, runtime, decoder settings, app build, and device when relevant. Record why the case exists and what regression it detects.
3. Separate deterministic and semantic review. Automate schema, required-field, boundary, and fallback checks. Use independent human review for meaning or safety properties that cannot be judged by simple exact string comparison.
4. Preserve all run outcomes. Store pass, fail, fallback, cancellation, and incomplete results with the same configuration labels. Compare to a pinned baseline and publish no score or ranking without raw data and a defined scoring method.

Keep each transition observable. A failure should identify the stage, artifact, runtime, and recovery action without logging private user content.

Acceptance criteria

• Every case maps to a user-visible requirement and has a versioned fixture and expected behavior.
• The report includes failures, fallback, and incomplete results, not only selected successful examples.
• Any published numerical claim can be reproduced from retained raw records and stated scoring rules.

Decision table

Failure patterns to prevent

• Changing multiple variables
• Judging fluency only

Also prevent silent fallback, unpinned artifacts, missing cancellation, and conclusions that combine unlike configurations. Store unsuccessful runs alongside successful ones.

Minimum reproducibility record

Release checklist

• ☐ The primary query is answered without an unsupported number.
• ☐ Every artifact and runtime is pinned.
• ☐ The representative task and failure policy are explicit.
• ☐ Lifecycle, cancellation, cleanup, and fallback are tested.
• ☐ User-content and network boundaries are documented.
• ☐ Result wording applies only to the recorded configuration.
• ☐ The page links to raw method or evidence when results are added.

Sources and related evidence

• Cove benchmark method
• FunctionGemma mobile actions
• Cove benchmark method and raw-data gate
• Google AI for Developers — FunctionGemma model card
• Android Developers — test apps on Android

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Frequently asked questions