Keypup

Dataset: Issues &amp; Pull Requests

Description: A list of keywords categorizing the type of work associated with the issue or pull request.

Issues: the combination of <code>labels</code> and <code>sub_type</code>.

Pull requests: the combination of <code>labels</code> from the pull request and <code>metatags</code> from resolved issues. Resolved issues are issues referenced by the pull request via <a href="https://docs.keypup.io/en/articles/8117180-linking-pull-requests-to-issues">auto-closing keywords</a>.

- Issues: the combination of <code>labels</code> and <code>sub_type</code>.
- Pull requests: the combination of <code>labels</code> from the pull request and <code>metatags</code> from resolved issues. Resolved issues are issues referenced by the pull request via <a href="https://docs.keypup.io/en/articles/8117180-linking-pull-requests-to-issues">auto-closing keywords</a>.

The <code>Metatags</code> field is one of the most powerful attributes for accurately categorizing work, as it provides a comprehensive list of all relevant keywords associated with an item. For pull requests, its unique ability to inherit the labels and types from the issues it resolves makes it superior to <code>Labels</code> for almost any kind of categorization.

Accurate Work Categorization: This is the primary use case for metatags. Since it includes tags from resolved issues, you can accurately categorize a pull request based on the problem it solves, not just the labels on the PR itself.

- Change Failure Rate (DORA Metric): You can build a highly accurate Change Failure Rate metric by identifying pull requests that fix bugs. A custom formula like <code>COUNT_IF(CONTAINS(metatags, "Bug"))</code> will correctly count a PR that resolves a Jira "Bug" issue, even if the PR itself is not labeled "Bug".
- Work Type Analysis: Create a pie chart to see the breakdown of your engineering efforts. Use a dimension with a formula like <code>IF(CONTAINS(metatags, "bug"), "Bug Fixes", "Feature Work")</code> to correctly categorize all merged pull requests.

Filtering and Scoping: You can create powerful reports that link development activity back to your planning process.

- Find all Bug-Fixing PRs: Create a report of all pull requests that address a bug by using a single filter: <code>Metatags contains "bug"</code>. This is much more reliable than filtering on PR labels alone.
- Track Work by Issue Type: Find all pull requests related to "Story" or "Task" issues from Jira with a filter like <code>Metatags contains any of "Story,Task"</code>.

Reporting on Individual Tags: Just like with label_names, you can use the <code>FLATTEN</code> function to analyze the prevalence of each individual tag across all your work items.

- To see a list of the most common tags (including inherited ones), create a report with <code>FLATTEN(metatags)</code> as the dimension and <code>COUNT()</code> as the metric.

- Accurate Work Categorization: This is the primary use case for metatags. Since it includes tags from resolved issues, you can accurately categorize a pull request based on the problem it solves, not just the labels on the PR itself.
 - Change Failure Rate (DORA Metric): You can build a highly accurate Change Failure Rate metric by identifying pull requests that fix bugs. A custom formula like <code>COUNT_IF(CONTAINS(metatags, "Bug"))</code> will correctly count a PR that resolves a Jira "Bug" issue, even if the PR itself is not labeled "Bug".
 - Work Type Analysis: Create a pie chart to see the breakdown of your engineering efforts. Use a dimension with a formula like <code>IF(CONTAINS(metatags, "bug"), "Bug Fixes", "Feature Work")</code> to correctly categorize all merged pull requests.
- Filtering and Scoping: You can create powerful reports that link development activity back to your planning process.
 - Find all Bug-Fixing PRs: Create a report of all pull requests that address a bug by using a single filter: <code>Metatags contains "bug"</code>. This is much more reliable than filtering on PR labels alone.
 - Track Work by Issue Type: Find all pull requests related to "Story" or "Task" issues from Jira with a filter like <code>Metatags contains any of "Story,Task"</code>.
- Reporting on Individual Tags: Just like with label_names, you can use the <code>FLATTEN</code> function to analyze the prevalence of each individual tag across all your work items.
 - To see a list of the most common tags (including inherited ones), create a report with <code>FLATTEN(metatags)</code> as the dimension and <code>COUNT()</code> as the metric.

Issues and PRs > Metatags

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From:
Github (PRs, Issues)	Inferred from self and resolved issues
Gitlab (PRs, Issues)	Inferred from self and resolved issues
Bitbucket (PRs)	Inferred from self and resolved issues
Azure DevOps (PRs, Issues)	Inferred from self and resolved issues
JIRA (Issues)	Calculated
ClickUp (Issues)	Calculated
Trello (Issues)	Calculated

Issues and PRs > Metatags

Reporting Use Cases