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When a feature looks different on two machines, check whether both runs evaluated the same environment, flag configuration, user identity, targeting rules and returned variation. A tester can be exercising variant B without realizing it—but assignment is only one possible cause; differences in code, data, browser state or deployment can also explain the mismatch.
Why the same feature can look different
Feature flags can control releases and rollouts as well as experiments. In an A/B experiment, an SDK evaluates a flag using the configuration it has loaded and the identity and attributes supplied by the application. Targeting rules and traffic allocation can therefore return different variations to different evaluated users. Optimizely’s Feature Experimentation overview describes these uses and includes environment and variation-preview capabilities.
“Variant B” is a useful shorthand for one possible explanation, not a diagnosis. Two machines may also differ in their build, deployment, data, browser state or other configuration. First establish what each run actually evaluated rather than assuming the flag is responsible.
What to record before reproducing the issue
Capture the context for both the working and failing runs. This makes it possible to spot a mismatch in configuration or identity before changing code.
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- Build or deployment being tested.
- Environment the application initialized against.
- Flag or experiment key and the expected rule and variation.
- User ID passed to the SDK and any relevant user attributes.
- Variation or decision the application actually received.
This checklist is a practical way to compare runs; the cited platform documentation does not prescribe this exact record format.
How to test a specific variation
Start with a stable test identity
Use a known identity for repeatable runs, and confirm that it is the same ID the application passes to the SDK. Google Cloud’s App Lifecycle Manager guidance describes using a randomized attribute such as userID for sticky bucketing and recommends descriptive string IDs for variants. That page is labeled Preview and subject to Pre-GA terms, so its guidance is specific to that product and status: Google Cloud App Lifecycle Manager feature flags.
If your application supports anonymous and logged-in identities, test the transition separately. How identity changes affect evaluation depends on the implementation; do not assume every SDK or application handles it the same way.
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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 the user IDs passed to the SDK. Optimizely documents a limit of up to 50 users per experiment and cautions that forcing many users into variations can bias experiment results. See Optimizely’s allowlisting documentation.
An allowlisted run is useful for verifying a path, but it is not a normal randomized sample. Keep forced or allowlisted identities out of conclusions about experiment performance. Other platforms may offer different QA controls; use the relevant SDK documentation rather than assuming this Optimizely mechanism is universal.
Check the environment and its configuration
Environments can have their own configuration and datafiles. Confirm which environment the running application actually initialized against; a dashboard setting by itself does not prove that a local build loaded the expected configuration. Optimizely documents Development and Production environments, separate datafiles, and additional environments for staged testing in its two- and three-stage testing guide.
Preproduction is useful for checking work in progress without exposing unfinished experiments to end users or contaminating production experiment data. Optimizely’s QA guidance recommends preproduction testing for those reasons: Choose QA tests.
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Choose the right testing method
| Method | What it is useful for | Important constraint |
|---|---|---|
| Allowlisting | Forcing selected Optimizely test identities into a chosen variation to verify behavior. | Bypasses normal targeting and allocation; IDs must match SDK input; up to 50 users per experiment. Forced assignment is not a representative randomized sample. Optimizely |
| Mocks or stubs | Testing a particular decision in unit or integration tests without relying on live assignment. | Keep tests focused on the paths that matter instead of multiplying every test by every flag combination. Optimizely |
| Preproduction environment | Checking staged changes against a non-production configuration. | Verify the environment and configuration loaded by the app; dashboard configuration alone does not establish what a local run used. Optimizely |
| QA audience or other platform-specific control | Restricting or selecting who can exercise a test path when the platform supports it. | Availability and behavior vary by platform; consult its documentation. Optimizely QA guidance |
Optimizely recommends keeping unit tests agnostic of flags where possible and mocking or stubbing decisions when a flag matters. Integration tests should focus on individual paths; reserve manual checks for business-critical variations rather than attempting every combination. Its guidance notes that traditional automated QA does not scale well as flag combinations grow: Choose QA tests.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Separate verification from experiment measurement
QA controls answer whether a path works; experiment assignment and measurement answer whether variations affect a metric. Do not treat a forced variation as evidence about the result of normal randomized assignment.
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When assignment or instrumentation is in doubt, an A/A test—where both variations behave the same—can help validate traffic splits and metric stability before a true A/B test. LaunchDarkly describes A/A testing and metrics connected to flag variations in its experimentation documentation. Follow the platform’s own analysis and instrumentation requirements when interpreting results.
Quick Recap
A practical troubleshooting sequence
- Compare the runs. Record the build, environment, flag key, user ID, relevant attributes and returned variation for each machine.
- Verify configuration. Check that each app instance loaded the intended environment configuration and that the relevant rule exists there.
- Check identity and targeting. Confirm the SDK input matches the identity expected by the rule or QA control, then inspect relevant attributes and allocation conditions.
- Reproduce deliberately. Use a stable identity for repeatability; if necessary, use a supported QA override in an appropriate environment to exercise the desired path.
- Keep the result in context. Treat a forced path as verification, not as a normal experiment sample. If the evaluated variation matches but the behavior still differs, investigate code, data, browser state and deployment differences.
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