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A script for measuring organizational metrics in projects.
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Conway's law

Any piece of software reflects the organizational structure that produced it.

A study that has been done at Microsoft concluded that

the prediction models built using organizational metrics against traditional code churn, code complexity, code coverage, code dependencies and pre-release defect measures to show that organizational metrics are better predictors of failure-proneness than the traditional metrics used so far.

This utility tries to measure some of the organizational metrics. Together with a code analysis tool it provides an accurate predictions of failure-proneness in software. It checks all files in GIT repository and show statistics for them.


To run the script, copy it to somewhere in $PATH and then run it from the directory of your project.

Example output:

Contributors: 24
Total edits: 83
1st contributor edits: 10
2nd contributor edits: 9

Contributors: 27
Total edits: 164
1st contributor edits: 25
2nd contributor edits: 12


Based on that, one can conclude that there are a few problems with those files.

  • The number of contributors is large, which means that the developer rotation in the organization is also probably too often which usually affects knowledge retention and thus quality.
  • There probably isn't a single person that has the responsibility over those files. (If the code is very clean - this could be a good thing)
  • Large number of total edits suggests that the code is not respecting the single responsibility principle.
  • Large number of total edits may also suggests that the requirements change a lot and/or there is a lack of stability.

To analyze the numbers one need to scale them accordingly to the size of the organization and the project. A project that last a year has different sensible value of total edits than a project that last 10 years.

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