Total Lines Added Metric
TBD
Definition
Total Lines Added measures the number of new lines of code inserted during the selected time range.
A “line added” is any line that did not previously exist in the file and was introduced in a commit that was later merged.
In AI Analytics views, this metric reflects lines added that are associated with the selected AI dimension (e.g., Coding Assistance).
How the Metric Is Calculated
Total Lines Added is calculated as:
Sum of all lines inserted across merged pull requests within the selected time range
This metric includes only newly inserted lines.
It does not include:
- Modified lines
- Deleted lines
In AI Analytics views, only lines attributed to the selected AI category are included.
Normalization
The headline value is normalized to the selected time bucket.
For example:
- Daily view → Lines added per day
- Weekly view → Lines added per week
- Monthly view → Lines added per month
The headline value represents:
Total lines added ÷ Number of selected time buckets
This allows comparison across time ranges.
How the Metric Is Displayed in the Dashboard
1. Headline Value (e.g., 48.3k on average per week)
Represents the average number of new lines inserted per selected time bucket.
This is not a total count.
2. Time-Based Values in the Chart
Each point represents:
The total number of new lines inserted within that specific time bucket
The chart shows actual per-bucket volume, not cumulative totals.
What This Metric Reflects
Total Lines Added reflects:
- Growth in codebase size
- New functionality introduced
- Expansion of system surface area
- AI-assisted code insertion (when filtered)
It does not reflect:
- Net code growth (see Lines Deleted)
- Total change magnitude (see Total Lines Accepted)
- Code quality
- Refactor vs rework distribution
Relationship to Other Metrics
Metric | Measures |
Total Lines Added | Only newly inserted lines |
Total Lines Deleted | Only removed lines |
Total Lines Accepted | Added + modified + deleted |
Code Changes | Overall change volume |
Total Lines Added isolates growth.
AI Analytics Context
When filtered by AI dimension:
- Reflects lines inserted with AI involvement.
- Attribution depends on configured AI detection logic.
- Partial AI involvement may still count toward attribution.
Data Sources
Derived from:
- Git diff metadata
- Pull request merge events
- Repository integration APIs
- AI attribution signals (if filtered)
Limitations
- Measures volume, not quality.
- Large generated files can inflate values.
- Does not distinguish between feature code and configuration code.
- Does not measure business impact.
- AI attribution depends on detection precision.
Stakeholder Use Cases
Engineering Managers
- Monitor codebase growth trends.
- Compare AI-assisted vs human-added code.
Team Leads
- Detect unusually large insertions.
- Monitor batch size trends.
DevOps / Architecture Teams
- Track system expansion velocity.
Product Leadership
- Assess growth rate of delivered functionality.
How did we do?
Total Lines Accepted Metric
Total Lines Deleted Metric
