2022 Feature Experimentation release notes
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November
New Feature Experimentation SDK
Released an SDK for Flutter
October
Optimizely Feature Experimentation Parity Items
The following have been released to Optimizely Feature Experimentation, increasing the feature parity between the new Optimizely Feature Experimentation and legacy Optimizely Full Stack Experimentation products.
August
Optimizely Feature Experimentation Parity Items
The following have been released to the Optimizely Feature Experimentation, increasing the feature parity between Feature Experimentation and the legacy version of Full Stack.
For more information on the Optimizely Feature Experimentation and Full Stack legacy feature parity work, refer to the developer documentation.
New Features
Created templates and starter kits to implement feature flagging and experimentation across major serverless edge compute platforms.
July
New Features
- Created a Helm Chart for running Optimizely Agent on Kubernetes.
Outlier Smoothing
Made a UI update to communicate better how Optimizely's Stats Engine handles outliers in your revenue metrics. There have not been any underlying changes in Optimizely's Stats Engine or how Optimizely calculates results. This update is purely a phrasing enhancement to improve your understanding of what happens to their outlier values.
Forced Decision Methods
Rolled out a set of new APIs for the Feature Experimentation SDKs that will make overriding and managing user-level flags, experiments, and delivery rules even more straightforward.
These new methods extend our OptimizelyUserContext object, which previously allowed you to make flag decisions and flag events for a specific user. Now we’ve taken things up a notch!
The new methods lets you do the following:
- setForcedDecision — Forces a user into a specific variation
- getForcedDecision — Returns the variation the user is forced into
- removeForcedDecision — Removes a user from a particular variation
- removeAllForcedDecisions — Removes a user from all forced variations
These Forced Decision methods make it even easier to set up automated testing and QA by forcing certain User IDs into specific variations regardless of audience conditions and previously configured traffic allocations.
For more detailed information, click on the SDK you are interested in to view the developer documentation on the Forced Decision methods:
Vulnerability and Python Support Upgrades
Optimizely Feature Experimentation will no longer officially support older versions of Python. These older versions do not provide the secure libraries needed for the Optimizely Python SDK. For example, Python version 3.4 support has ended due to a known security vulnerability in the PyYAML library.
Optimizely supports the following versions:
- Python 3.7
- Python 3.8
- Python 3.9
- Python 3.10 and above
- PyPy3
Allowlisting
Allowlisting is now available to all customers. Allowlisting, previously known as whitelisting, was only available in the previous version of Full Stack.
Allowlisting lets you force certain users into a specific variation of an experiment. This capability can be beneficial during the QA process of development. See the developer documentation for additional helpful QA scenarios and steps to enable Allowlisting in your flag rules.