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…on experiment in experiments.json
Summary of ChangesHello @surapunoyousei, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request updates the Highlights
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Pull request overview
This PR replaces the workspace_archive experiment with a new chrome_web_store_install experiment in the experiments configuration. The new experiment enables installation of Chrome Web Store extensions with a gradual rollout strategy starting at 10%.
Key changes:
- Replaced the fully rolled out (100%) workspace_archive experiment with a new chrome_web_store_install experiment
- Set rollout to 10% for gradual deployment of the Chrome Web Store installation feature
- Added a "name" field to the experiment variant for improved clarity
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| "variants": [ | ||
| { | ||
| "id": "enabled", | ||
| "name": "Enabled", |
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The variant now includes a "name" field ("Enabled") which is inconsistent with other experiments in the file. The "pwa_taskbar_integration_linux" experiment (lines 18-31) doesn't have a "name" field in its variant. Either add the "name" field to all experiment variants for consistency, or remove it from this one.
| "name": "Enabled", |
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Code Review
This pull request updates the experiments configuration file, replacing an existing experiment with a new one for 'chrome_web_store_install'. My review focuses on improving the experimental design. I have provided a suggestion to introduce a dedicated control group, which will allow for a more robust and accurate analysis of the feature's impact.
| "variants": [ | ||
| { | ||
| "id": "enabled", | ||
| "name": "Enabled", | ||
| "weight": 100 | ||
| } | ||
| ] |
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The current experiment configuration sets a 10% rollout with a single 'enabled' variant at 100% weight. While this is suitable for a simple staged rollout, it lacks a dedicated control group within the experiment population. For more reliable data analysis and to accurately measure the feature's impact against a baseline, it's a best practice to include a control group.
By splitting the 10% population into a treatment group and a control group (e.g., 50/50 split), you can create two comparable groups of users, which helps isolate the feature's effects from other variables and provides more trustworthy results.
"variants": [
{
"id": "enabled",
"name": "Enabled",
"weight": 50
},
{
"id": "control",
"name": "Control",
"weight": 50
}
]
…on experiment in experiments.json