Dataset: Reviews from pull requests
Entity: Pull Request Review
Field ID: created_at
Type: Datetime
Description: The datetime at which the review was created.
Source: App
Transformation logic: For GitLab and Bitbucket, this value is always identical to submitted_at in the Reviews dataset, as these apps do not track the timestamp of the beginning of a review.
From: |
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Github (Reviews) |
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Gitlab (MRs) |
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Bitbucket (PRs) |
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Azure DevOps (PR Threads) |
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Jira | N/A |
ClickUp | N/A |
Trello | N/A |
Reporting Use Cases
The Created At field from the Reviews dataset is a crucial timestamp, especially for GitHub, as it marks the moment a reviewer begins their review (e.g., by starting a draft). It serves as the starting point for measuring the active review phase and the responsiveness of your review team.
Calculating Review Durations (GitHub-Specific): This field's most powerful use is to isolate and measure different parts of the review cycle on GitHub.
Time to Start Review (Idle Time): You can calculate how long a pull request waits for a reviewer to begin their work with the custom formula
(created_at - review_requested_at) / HOUR(). This is a direct measure of your team's responsiveness to review requests.Active Review Time: You can measure how long a reviewer actively spends on their review (from starting the draft to submitting it) with the formula
(submitted_at - created_at) / HOUR().
Filtering for In-Progress Reviews: You can create a report to find reviews that have been started but not yet submitted, which might indicate a reviewer is blocked or has forgotten to finalize their feedback.
A filter where
Created At is not null and Submitted At is nullwill show all currently in-progress reviews.
Reporting on Review Activity: When used as a dimension, this field can track the volume of reviews being initiated over time.
A line chart with a dimension like
YEAR_MONTH(created_at)and aCOUNT()metric can show you how many new reviews your team starts each month, providing insight into the review workload being picked up.