Choose QA tests for Feature Experimentation
- Optimizely Feature Experimentation
Before launching your experiment, you should verify it is running correctly. This is called Quality Assurance (QA).
Best practices for what to test
The traditional method for automating QA tests do not scale with a large number of flags. If you try to unit test, integrate test, and end-to-end test every combination of every flag and its variations, you get an explosion of tests.
At a high level, the following is some guidance for automating tests selectively:
- Unit tests – Should be agnostic of flags. If a unit test has to be aware of flags, then mock and stub flags.
- Integration tests – Should also have as little awareness of flags as possible. Focus on individual code paths to ensure proper business logic and integration. Force the particular flag variations you want to test by using mocks and stubs. For example, you can mock a Decide call to return true in an integration test.
- Manual verification – Expensive, so reserve a human QA tester for business-critical variations and flags.
- End-to-end tests – These are the most expensive to write and maintain, so reserve these only for the most business-critical experimental or flag paths. Include a test that checks what happens if all flags are enabled. Include another test to check that the system can degrade gracefully in the unlikely event your flag system goes down.
QA checklist
The following is a QA checklist for launching an experiment:
- Start your test in a non-production environmentnon-production environment.
- Force yourself into each experience using a QA tool.
- Trigger any conversion events that are being tracked by the test and are on the same page as a variation change. Testing pre-existing or down-funnel events may not be necessary unless those events also change.
- If you are unable to see your testing activity affect the results page, this may be due to how Optimizely counts conversions. Conversions are attributed to the first variation assignment. It may be necessary to reset results in between testing your variations.
- If step four does not produce the results you expected, troubleshoot by increasing the log level and consulting your error handler to debug.
- Repeat steps two through five for each variation of your test.
- Confirm your results. There is a five-minute interval on results page updates, so you might have to wait up to five minutes for it to update. The time of the last update is shown on the results page.
- Duplicate the non-production environment's rulesDuplicate the non-production environment's rules to your production environment rules. Each environment has its own results on the reports pagereports page.
- Launch in your production environment.
Which QA tool to choose
Depending on your testing needs, Optimizely Feature Experimentation offers a variety of QA tools to choose from.
| Use an allowlistUse an allowlist | Use a QA audienceUse a QA audience | Use forced bucketingUse forced bucketing |
|---|---|---|---|
Granularity | * Flag variables | | |
- Flag variations
- Experiments | Experiments | Flag variations | | Example use cases | - Mocks during development.
- Allowlist a test runner so you see the variation you want the test to assert against. | * Mocks during development
- Automated web UI test
- Manual web UI test | Tests run on a behavior-driven development (BDD) framework. | | Ease of use | Easiest. It can override all other configurations, allowing you to leave your intended flag rule configuration intact while you QA. | Medium. Requires some flag rule configuration changes. | Harder | | Available for | A/B experiments. | - A/B tests.
- Targeted deliveries | A/B experiments. | | Comments | You can only allowlist up to 50 user IDs per experiment. | Two easy implementations are to audience match on:1) A URL query parameter.
- A cookie. | |
Keep your experiment data clean
The preceding QA tools are for flag rules that are already running or enabled. But how do you prevent end users from exposing themselves to your in-development, running experiment? You can run your experiment in a preproduction environmentpreproduction environment.
Feature Experimentation projects automatically come with a Development and Production environment. Each environment has its own results on the Optimizely Experiment results pageOptimizely Experiment results page. After completing QA testing in your preproduction environment, copy your rules to the production environment and run your experiments on your target audience. See Duplicate rules across environmentsDuplicate rules across environments.