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Comments > Body

Written by Tom Williams

Dataset: Comments

Entity: Comments

Field ID: body

Type: Text

Description: The body of the comment in source format.

You can ask us not to import comment bodies. If you would rather Keypup did not store the text of your comments, contact our support team and we will skip them. The same applies to pull request and issue bodies. When bodies are skipped, this field is empty.

Source: App

Transformation logic: For Jira, the rendered HTML body is converted to Markdown.

App Mapping

Github (Comments)

body

Gitlab (Comments)

body

Bitbucket (Comments)

content

Azure DevOps (Comments)

PR: content

Issue: text

Jira (Comments)

renderedBody, converted to Markdown

ClickUp (Comments)

comment_text

Trello (Comments)

text

Reporting Use Cases

The Body field, containing the full text of a comment, is a rich source of qualitative data that can be used to understand the nature of discussions, track action items, and even create informal metrics on platforms that lack a formal review system.

  • Filtering for Context: The most direct use of this field is to find specific conversations by searching for keywords.

    • Find Specific Discussions: You can create a report of all comments related to a specific topic by using a filter like Body ~ "security".

    • Isolate Action Items: Identify comments that contain follow-up tasks by filtering where Body ~ "(?i)TODO|action item".

    • Exclude Automated Noise: Filter out comments generated by bots to focus on human interaction, for example, Body !~ "This comment was generated automatically".

  • Custom Formulas for Categorization: You can use custom formulas to classify comments based on their content, turning unstructured text into a structured dimension for reporting.

    • Comment Type Analysis: A custom dimension with a formula like IF(body ~ "(?i)lgtm|approved", "Approval Comment", "Feedback Comment") can help you categorize and count the different types of feedback your team provides. This is especially useful for GitLab and Bitbucket, which lack a formal "commented" vs. "approved" review state.

  • Custom Formulas for Advanced Metrics: You can create KPIs by counting comments that contain specific keywords.

    • Informal Approval Rate: You can approximate an approval rate by creating a metric with the formula COUNT_IF(body ~ "(?i)lgtm").

    • Question Ratio: Measure how many comments are questions, which could indicate a need for clearer pull request descriptions, with a metric like COUNT_IF(body ~ "\\?").

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