If a feature works on your machine but not on a teammate’s, check which flag environment, user identity, targeting rules and variation each machine actually evaluated. A/B tests and feature flags can intentionally serve different paths to different users. That makes variant assignment a useful place to investigate—not the only possible cause of a mismatch.
Why the same build can show different behavior
A feature flag can decide which code path a running app takes, while an experiment can assign an evaluated user to a variation. Those decisions depend on the flag’s rules and the identity and attributes passed to the SDK. Two machines running apparently identical code may therefore display different behavior if they use different environments, identities or targeting context. Optimizely’s Feature Experimentation overview describes flags used for releases, experiments and rollouts.
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“Variant B” is shorthand for one possible explanation, not a diagnosis. Differences in deployment, data, browser state or other configuration can produce similar symptoms. First establish what each app actually loaded and evaluated.
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What to record before reproducing the problem
Capture enough context to compare the two runs rather than relying on “it works for me.” A useful record includes:
- Build or deployment identifier.
- Environment the app initialized against, such as development, preproduction or production.
- Flag or experiment key and the rule expected to match.
- Expected variation and the variation the SDK actually returned.
- User ID and any user attributes relevant to targeting.
This checklist is a practical way to make the comparison reproducible; it is not a prescribed vendor form. Environment-specific configuration matters: Optimizely documents separate environments and datafiles, so confirm the environment used by the running app rather than assuming a dashboard selection proves what a local app loaded. Optimizely environments and its two- and three-stage testing guidance describe this separation.
How to test a specific variation without confusing QA and measurement
Use a stable test identity
For repeatable checks, run with a known user identity and verify that it is the same ID the SDK evaluates. If the app supports anonymous and logged-in states, test the identity transition separately; whether and how assignment changes depends on the implementation. Google Cloud’s App Lifecycle Manager documentation describes using a randomized attribute such as userID for sticky bucketing and recommends descriptive variant IDs. That page is marked Preview and subject to Pre-GA terms, so treat it as platform-specific guidance. Google Cloud App Lifecycle Manager.
Use an explicit QA control when you need to force a path
In Optimizely Feature Experimentation, an allowlist can assign selected user IDs to chosen variations, bypassing normal audience targeting and traffic allocation. The IDs must match those passed to the SDK, and the documented limit is 50 users per experiment. Optimizely cautions that broad forced bucketing can bias results. Use this mechanism to verify a path, not to create a supposedly representative experiment sample. Optimizely allowlisting documentation.
That control is specific to Optimizely; other platforms may provide different QA mechanisms. Follow the documentation for the SDK and configuration actually used by your app.
Keep QA in a suitable environment
Preproduction lets a team check in-development experiments without exposing them to end users or contaminating production experiment data. Optimizely recommends preproduction testing for this purpose. Confirm that the app under test initializes against the intended environment and its configuration; checking a setting in the dashboard alone is not enough to establish what the app loaded. Optimizely’s QA test guidance.
Choose the right test at each layer
Unit tests: isolate the decision
Where possible, keep unit tests agnostic of flag state. When a flag changes behavior that matters to the unit, mock or stub the decision so the test exercises a controlled path rather than depending on live assignment.
Integration tests: cover important paths selectively
Use mocks or stubs to force the relevant decisions in integration tests. Avoid multiplying tests across every possible combination of flags; reserve broader manual verification for business-critical variations. Optimizely’s guidance discusses these trade-offs and warns that traditional automated QA does not scale cleanly as flag counts grow. Choose QA tests.
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Experiment checks: validate assignment and instrumentation
If you suspect the problem is not just a UI path but the experiment setup itself, verify that assignment and measurement behave as intended. LaunchDarkly describes A/A tests, in which the variations behave the same, as a way to check traffic splits and metric stability before a true A/B test. Its experimentation documentation also explains connecting metrics to flag variations. Follow the platform’s own analysis requirements before interpreting results. LaunchDarkly Experimentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical comparison when picking a QA method
| Method | What it helps verify | Effect on normal assignment | Important constraint |
|---|---|---|---|
| Allowlist (Optimizely Feature Experimentation) | A selected identity’s chosen variation | Bypasses normal targeting and traffic allocation | Up to 50 IDs per experiment; IDs must match SDK input; forced bucketing can bias results. Source |
| Mocks or stubs | Application behavior for a controlled flag decision | Does not test live assignment | Useful in unit and integration tests; select important paths rather than every combination. Source |
| Preproduction environment | In-development experiments before exposure to end users | Uses the environment’s configuration rather than production assignment | Confirm which environment and datafile the app actually loads. Source |
| A/A experiment check (LaunchDarkly) | Traffic split and metric stability when variations behave the same | Checks the experiment flow rather than forcing a desired variation | Use the platform’s method and analysis requirements. Source |
How to interpret the result
If two runs differ, compare the environment, SDK identity and attributes, matching rule, and returned variation first. If those match, widen the investigation to deployment, data, browser state and other configuration. A forced QA path answers whether that variation works; it does not establish how randomized users are assigned or whether experiment results are unbiased.
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