A Python script to compute "true" code churn of a Git repository. Useful for software teams to openly help manage technical debt.

Code churn has several definitions, the one that to me provides the most value as a metric is:

"Code churn is when an engineer rewrites their own code in a short period of time."

Reference: https://www.pluralsight.com/blog/teams/why-code-churn-matters

Solutions that I've found online looked at changes to files irrespective whether these are new changes or edits to existing lines of code (LOC) within existing files. Hence this solution that segments line-of-code edits (churn) with new code changes (contribution).

Tested with Python version 3.5.3 and Git version 2.20.1

This script looks at a range of commits per author. For each commit it bookkeeps the files that were changed along with the LOC for each file. LOC are kept in a sparse structure and changes per LOC are taken into account as the program loops. When a change to the same LOC is detected it updates this separately to bookkeep the true code churn. Result is a print with aggregated contribution and churn per author for a given time period.

Note: This includes the --no-merges flag as it assumes that merge commits with or without merge conflicts are not indicative of churn.

Positional (required) arguments:

• after        after a certain date, in YYYY[-MM[-DD]] format
• before     before a certain date, in YYYY[-MM[-DD]] format
• author     author string (not committer)
• dir            include Git repository directory

Optional arguments:

• -h, --h, --help    show this help message and exit
• -exdir                   exclude Git repository subdirectory

python ./gitcodechurn.py after="2018-11-29" before="2019-03-01" author="an author" dir="/Users/myname/myrepo" -exdir="excluded-directory"

author:       an author
contribution: 844
churn:        -28

Outputs can be used as part of a pipeline that generates bar charts for reports: