Dataset: Issues & Pull Requests
Entity: Pull Requests
Field ID: review_state
Type: Select list
Description: The state of the pull request review. Possible values are:
APPROVEDthe pull request is approved by all participating reviewersCHANGES_REQUESTEDone of the reviewers has requested changesNONEthere are no reviews in progressPENDINGsome required reviews are still pending approval/rejection
Source: Calculated
Transformation logic:
Pull requests:
No assigned reviewers =>
NONEOne review is requesting changes =>
CHANGES_REQUESTEDThe number of approving reviews is less than the required approval count =>
PENDINGThe number of approving reviews is greater than or equal to the required approval count =>
APPROVED
Issues: this field is always
null
From: |
|
Github (PRs) | Calculated |
Gitlab (PRs) | Calculated |
Bitbucket (PRs) | Calculated |
Azure DevOps (PRs, Issues) | Calculated |
JIRA (Issues) | N/A |
ClickUp (Issues) | N/A |
Trello (Issues) | N/A |
Reporting Use Cases
The Review State field provides a real-time, high-level summary of a pull request's status in the review process, making it one of the most important fields for building actionable dashboards, managing workflow, and identifying bottlenecks.
Filtering for Actionable Work Queues: This field's primary use is to filter and segment pull requests into clear, actionable queues, which is the foundation of many Kanban-style boards.
"To Review" Queue: Create a list for reviewers by filtering where
Review State = "PENDING"."Needs Rework" Queue: Build a list for developers of all PRs that require changes by filtering where
Review State = "CHANGES_REQUESTED"."Ready to Merge" Queue: Identify pull requests that are ready for the final deployment step by combining filters where
Review State = "APPROVED" and Build Status = "SUCCESS".
Reporting on Pipeline Health: Using review_state as a dimension gives you a powerful, at-a-glance overview of your entire review pipeline.
A pie chart with
Review Stateas the dimension andCOUNT()as the metric will instantly show the distribution of your open pull requests. A large slice forPENDINGorCHANGES_REQUESTEDis a clear indicator of a bottleneck.
Creating Advanced KPIs: You can use this field within custom formulas to track the health of your review process over time.
Changes Requested Rate: Monitor the percentage of open pull requests that are currently blocked and waiting for rework with a custom formula like
ROUND(COUNT_IF(review_state == "CHANGES_REQUESTED") / COUNT() * 100, 2). A rising trend in this metric can signal issues with initial code quality or unclear requirements.