Total Lines Accepted Metric

Definition

Total Lines Accepted measures the total number of code lines that were accepted into the main branch during the selected time range.

“Accepted” refers to lines of code that were merged into the target branch.

This metric includes:

• Lines added
• Lines modified
• Lines deleted

Only lines that were successfully merged are counted.

How the Metric Is Calculated

Total Lines Accepted is calculated as:

Sum of (lines added + lines modified + lines deleted) across all merged pull requests within the selected time range

In AI Analytics views, this calculation is further filtered based on the selected AI dimension (e.g., Coding Assistance, AI Coding, Code Review).

Normalization

The headline value is normalized to the selected time bucket.

For example:

• Daily view → Lines per day
• Weekly view → Lines per week
• Monthly view → Lines per month

The headline value represents:

Total accepted lines ÷ Number of selected time buckets

This ensures comparability across time ranges.

How the Metric Is Displayed in the Dashboard

The metric card displays two values:

1. Headline Value (e.g., 63.2k on average per week)

Represents the average number of lines accepted per selected time bucket.

This is not a total count.

2. Time-Based Values in the Chart

Each chart point represents:

The total number of accepted lines within that specific time bucket

The chart reflects actual volume per bucket, not cumulative totals.

What This Metric Reflects

Total Lines Accepted reflects:

• Code volume integrated into production branches
• Accepted throughput
• Delivered change magnitude

It does not measure:

• PR count (see: Merge Frequency)
• Commit frequency (see: Commits)
• Code quality
• Review thoroughness

Relationship to Other Metrics

Total Lines Accepted is closely related to:

• Code Changes (overall change volume)
• Total Lines Added
• Total Lines Deleted
• Merge Frequency

Difference from Code Changes:

• Code Changes may include non-merged or filtered activity depending on view.
• Total Lines Accepted strictly reflects merged code.

AI Analytics Context

When used in AI Analytics:

• The metric reflects only lines associated with the selected AI dimension.
• For example, filtering by “Coding Assistance” includes only lines attributed to that source.

Attribution logic depends on configured AI detection mechanisms.

Data Sources

Derived from:

• Git diff metadata
• Pull request merge events
• Repository integration APIs
• AI attribution signals (when applicable)

Limitations

• Measures volume, not impact.
• Large refactors can inflate the metric.
• Does not distinguish between new functionality and structural changes.
• Does not measure code quality.
• AI attribution depends on detection confidence.

Stakeholder Use Cases

Engineering Managers

• Monitor delivered code volume.
• Compare human vs AI-assisted throughput.

Team Leads

• Evaluate batch size trends.
• Identify unusually large integrations.

DevOps / Platform Teams

• Assess release impact magnitude.

Product Leadership

• Track magnitude of delivered work.