Keypup

Dataset: Reviews from pull requests

Description: The state of the review

<code>APPROVED</code> the review approves the changes made in the pull request

<code>CHANGES_REQUESTED</code> the review requests some changes to the pull request (GitHub/Bitbucket).

<code>COMMENTED</code> the review is informational and does not intend to approve nor reject the pull request (GitHub only).

<code>DISMISSED</code> the review has been dismissed and is no longer applicable (GitHub only).

<code>PENDING</code> the review is in progress but has not been submitted yet (GitHub only).

- <code>APPROVED</code> the review approves the changes made in the pull request
- <code>CHANGES_REQUESTED</code> the review requests some changes to the pull request (GitHub/Bitbucket).
- <code>COMMENTED</code> the review is informational and does not intend to approve nor reject the pull request (GitHub only).
- <code>DISMISSED</code> the review has been dismissed and is no longer applicable (GitHub only).
- <code>PENDING</code> the review is in progress but has not been submitted yet (GitHub only).

The <code>State</code> field from the Reviews dataset is a critical attribute that captures the outcome of an individual review (e.g., <code>APPROVED</code>, <code>CHANGES_REQUESTED</code>). It is the primary tool for analyzing the nature of feedback, understanding reviewer behavior, and auditing your team's code review process.

Filtering by Review Outcome: You can create highly specific reports by filtering on the state of the reviews.

- Find All Rejections: To see a list of all reviews where changes were requested, you can use a filter like <code>State = "CHANGES_REQUESTED"</code>. This can help you identify PRs that required rework.
- Track Approvals: To get a list of all approving reviews, you can filter where <code>State = "APPROVED"</code>.

Reporting on Feedback Patterns: Using state as a dimension is the most effective way to understand the type of feedback your team provides.

- Review Distribution: A pie chart with <code>State</code> as the dimension and <code>COUNT()</code> as the metric will show you the proportion of reviews that are approvals, rejections, or simple comments. A high percentage of <code>CHANGES_REQUESTED</code> might indicate issues with initial code quality, while a very high percentage of <code>APPROVED</code> could suggest a less critical review culture.
- Feedback by Reviewer: A stacked bar chart with <code>Author</code> as one dimension and state as the second will show you the feedback patterns of each individual reviewer, highlighting who tends to approve versus who is more likely to request changes.

Custom Formulas for Advanced KPIs: You can use this field to create metrics that measure the efficiency of your review process.

- Approval Rate: You can calculate the overall approval rate with a custom formula like <code>ROUND(COUNT_IF(state == "APPROVED") / COUNT() * 100, 2)</code>.

- Filtering by Review Outcome: You can create highly specific reports by filtering on the state of the reviews.
 - Find All Rejections: To see a list of all reviews where changes were requested, you can use a filter like <code>State = "CHANGES_REQUESTED"</code>. This can help you identify PRs that required rework.
 - Track Approvals: To get a list of all approving reviews, you can filter where <code>State = "APPROVED"</code>.
- Reporting on Feedback Patterns: Using state as a dimension is the most effective way to understand the type of feedback your team provides.
 - Review Distribution: A pie chart with <code>State</code> as the dimension and <code>COUNT()</code> as the metric will show you the proportion of reviews that are approvals, rejections, or simple comments. A high percentage of <code>CHANGES_REQUESTED</code> might indicate issues with initial code quality, while a very high percentage of <code>APPROVED</code> could suggest a less critical review culture.
 - Feedback by Reviewer: A stacked bar chart with <code>Author</code> as one dimension and state as the second will show you the feedback patterns of each individual reviewer, highlighting who tends to approve versus who is more likely to request changes.
- Custom Formulas for Advanced KPIs: You can use this field to create metrics that measure the efficiency of your review process.
 - Approval Rate: You can calculate the overall approval rate with a custom formula like <code>ROUND(COUNT_IF(state == "APPROVED") / COUNT() * 100, 2)</code>.

Reviews > State

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From:
Github (Reviews)	`state` `APPROVED` => `APPROVED` `CHANGES_REQUESTED` => `CHANGES_REQUESTED` `COMMENTED` => `COMMENTED` `DISMISSED` => `DISMISSED` `PENDING` => `PENDING`
Gitlab (MRs)	Parse approval and changes requested event messages.
Bitbucket (PRs)	`pull_request.participants.*.state` `approved` => `APPROVED` `changes_requested` => `CHANGES_REQUESTED`
Azure DevOps (PR Threads)	`properties.CodeReviewVoteResult.$value` `10` => `APPROVED` `5` => `APPROVED` `-5` => `COMMENTED` `-10` => `CHANGES_REQUESTED`
Jira	N/A
ClickUp	N/A
Trello	N/A

Reviews > State

Reporting Use Cases