Dataset: Issues & Pull Requests
Entity: Pull Requests, Issues
Field ID: author_username
Type: Text
Description: The username of the person who created the issue or pull request. Note that usernames are app-specific.
Source: App
Transformation logic: N/A
From: |
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Github (PRs, Issues) |
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Gitlab (PRs, Issues) |
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Bitbucket (PRs) |
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Azure DevOps (PRs, Issues) |
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Jira (Issues) |
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ClickUp (Issues) |
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Trello (Issues) |
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Reporting Use Cases
The author_username field is essential for understanding the origin of work and analyzing contribution patterns across your team and projects. As a text field, it is most commonly used for filtering data and as a dimension for grouping results in reports.
Filtering and Personalization: You can easily scope your widgets to focus on work created by specific individuals or groups.
User-Specific Reports: Create reports that only show issues or pull requests created by a particular person using a filter like
Author = "jane.doe".Personal Dashboards: Use the is me operator to build dynamic widgets that show only the work initiated by the person viewing the dashboard.
Excluding Automated Work: A common use case is to filter out automated contributions by excluding authors that match a certain pattern, such as
Author !~ "bot".
Contribution Analysis: Using
Authoras a dimension in charts allows you to visualize who is creating work and what kind of work they are creating.Work Distribution: A bar or pie chart with author_username as the dimension and
COUNT()as the metric can quickly show who is opening the most issues or creating the most pull requests.Contribution Type: By adding a second dimension, such as the issue type or labels, you can analyze the nature of contributions. For example, you could create a stacked bar chart to see the breakdown of bugs versus feature requests submitted by each author.
Aggregated Metrics: You can also use this field within metrics to calculate higher-level KPIs.
Contributor Count: The
COUNT_DISTINCT(author_username)formula will tell you the number of unique contributors in a given period, which is a great way to measure engagement and team growth.Anonymized Reporting: If you need to report on trends without showing individual names, you can use hashing functions like
SHA1(author_username)to anonymize the data while still being able to group by unique individuals.