Dataset: Comments
Entity: Comments
Field ID: body
Type: Text
Description: The body of the comment in source format.
Source: App
Transformation logic: N/A
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Github (Comments) |
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Gitlab (Comments) |
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Bitbucket (Comments) |
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Azure DevOps (Comments) |
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JIRA (Comments) |
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ClickUp (Comments) |
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Trello (Comments) |
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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 ~ "\\?").