Keypup

Dataset: Reviews from pull requests

Field ID: <code>author_username</code>

Description: The username of the person who created the review. Note that usernames are app-specific.

The <code>Author</code> field from the Reviews dataset is the key attribute for understanding your code review process, as it identifies the person who performed the review. It is essential for analyzing review workload, identifying key contributors to code quality, and tracking team engagement.

Filtering for Workload Management: You can create highly focused reports on review activity.

- Individual Review Log: To see all reviews performed by a specific team member, you can use a filter like `Author = "jane.doe".
- Personal Dashboard: Use the <code>is me</code> operator to create a "My Reviews" widget that shows all the reviews you have completed.
- Exclude Automated Feedback: If you have bots that leave automated review comments, you can exclude them to focus on human feedback with a filter like <code>Author !~ "bot"</code>.

Reporting on Review Distribution: Using <code>Author</code> as a dimension is the primary way to visualize how the review workload is distributed across your team.

- Top Reviewers: A bar chart with <code>Author</code> as the dimension and <code>COUNT()</code> as the metric will instantly show who is performing the most code reviews. A significant imbalance could indicate a knowledge silo or a potential bottleneck.
- Review Outcomes: You can analyze the type of feedback each reviewer gives by creating a stacked bar chart with <code>Author</code> as one dimension and <code>State</code> as the second. This can reveal who is more likely to request changes versus approve pull requests.

Custom Formulas for Advanced KPIs: You can aggregate this field to get a higher-level view of your review culture.

- Size of Reviewer Pool: A KPI with the custom formula <code>COUNT_DISTINCT(author_username)</code> can measure the number of unique reviewers participating in the process, which is a great indicator of shared ownership and knowledge transfer.

- Filtering for Workload Management: You can create highly focused reports on review activity.
 - Individual Review Log: To see all reviews performed by a specific team member, you can use a filter like `Author = "jane.doe".
 - Personal Dashboard: Use the <code>is me</code> operator to create a "My Reviews" widget that shows all the reviews you have completed.
 - Exclude Automated Feedback: If you have bots that leave automated review comments, you can exclude them to focus on human feedback with a filter like <code>Author !~ "bot"</code>.
- Reporting on Review Distribution: Using <code>Author</code> as a dimension is the primary way to visualize how the review workload is distributed across your team.
 - Top Reviewers: A bar chart with <code>Author</code> as the dimension and <code>COUNT()</code> as the metric will instantly show who is performing the most code reviews. A significant imbalance could indicate a knowledge silo or a potential bottleneck.
 - Review Outcomes: You can analyze the type of feedback each reviewer gives by creating a stacked bar chart with <code>Author</code> as one dimension and <code>State</code> as the second. This can reveal who is more likely to request changes versus approve pull requests.
- Custom Formulas for Advanced KPIs: You can aggregate this field to get a higher-level view of your review culture.
 - Size of Reviewer Pool: A KPI with the custom formula <code>COUNT_DISTINCT(author_username)</code> can measure the number of unique reviewers participating in the process, which is a great indicator of shared ownership and knowledge transfer.

Reviews > Author

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From:
Github (Reviews)	`author`
Gitlab (MRs)	`review_event.author`
Bitbucket (PRs)	`pull_request.participants.*.user`
Azure DevOps (PR Threads)	`comments.0..author`
Jira	N/A
ClickUp	N/A
Trello	N/A

Reviews > Author

Reporting Use Cases