Dataset: Issues & Pull Requests
Entity: Pull Requests
Field ID: past_reviewer_usernames
Type: List of text values
Description: The list of people's usernames who completed a review (even if dismissed) on the pull request. Note that usernames are app-specific.
Source: App
Transformation logic:
Pull Requests: The list of author usernames from completed pull request reviews. It includes
DISMISSED,COMMENTED,APPROVED, andCHANGES_REQUESTEDreviews. It excludesPENDINGreviews.Issues: N/A
From: |
|
Github (PRs, Issues) | Calculated |
Gitlab (PRs, Issues) | Calculated |
Bitbucket (PRs, Issues) | Calculated |
Azure DevOps (PRs, Issues) | Calculated |
JIRA (Issues) | N/A |
ClickUp (Issues) | N/A |
Trello (Issues) | N/A |
Reporting Use Cases
The Past Reviewers field provides a complete history of every individual who has submitted a review on a pull request, making it the definitive source for analyzing your team's review workload and collaboration patterns over time.
Filtering for Historical Analysis: You can create reports based on the full history of review participation, not just who is currently assigned.
Total Review Workload: To see every pull request a specific person has ever reviewed, you can use a filter like
Past Reviewers contains "john.doe". This is invaluable for understanding an individual's historical contribution to code quality.Identify Reviewed PRs: To focus a report only on pull requests that have undergone at least one review cycle, you can filter where
Past Reviewers length > 0.
Reporting on Review Workload Distribution: To accurately measure the review effort of each team member, you must use the
FLATTENfunction to treat each reviewer in the list as a separate entity.Reviews per Person: The most common use case is to create a bar chart showing the number of reviews each person has completed. Use a custom formula dimension like
FLATTEN(past_reviewer_usernames)with aCOUNT()metric to visualize who is carrying the largest share of the review workload.
Custom Formulas for Deeper Insights: You can analyze the breadth and depth of your review process with more advanced calculations.
Reviewer Collaboration: The formula
AVG(LENGTH(past_reviewer_usernames))calculates the average number of unique reviewers per pull request, giving you insight into how collaborative your review process is.Size of the Reviewer Pool: You can measure the total number of team members participating in reviews with the formula
COUNT_DISTINCT(FLATTEN(past_reviewer_usernames)). Tracking this metric over time can show whether knowledge sharing is increasing or if a small group of seniors is handling all the reviews.