codemetrics 0.5.1

SCM mining utility classes

Code Metrics

Code metrics is a simple Python module that leverage the libraries below to generate insight from a source control management (SCM) tool:

• pandas: for data munching.

• lizard: for code complexity calculation.

• cloc.pl (script): for line counts from cloc

• and your SCM: for now, only Subversion is supported. Looking to add git.

It can generate reports based on Adam Tornhill awesome books.

Installation

To install codemetrics, simply use pip:

pip install codemetrics

Usage

This is a simple tool that makes it easy to retrieve information from your Source Control Management (SCM) repository and hopefully gain insight from it.

The reports available for now are:

• AgeReport: help see what files/component has not changed in a while or who

  is most familiar with a particular set of files.

• HotSpotReport: combines line count from cloc with SCM information to identify

  files/components that are complex (many lines of code) and that change often. There are ways to post process the SCM log so that you adjust for mass edits or intraday changes.

• CoChangeReport: help identify what file/component changes when another part

  of the code base change. This is useful to identify hidden dependencies.

Recipes

Derive components from path

df['component'] = df['path'].str.split('\\').str.get(-2)

Will add a component column equal to the parent folder of the path. If no folder exists, it will show N/A.

For more advanced manipulation like extractions, see Pandas documentation

Aggregate hotspots by component

hotspots_report = cm.HotSpotReport('.')
log, cloc = hotspots_report.get_log(), hotspots_report.get_cloc()
cloc['component'] = cloc['path'].str.split('\\').str.get(-2)
log['component'] = log['path'].str.split('\\').str.get(-2)
hspots = hotspots_report.generate(log,
                                  cloc.groupby('component').sum().reset_index(),
                                  by='component').dropna()
hspots.set_index(['component']).sort_values(by='score', ascending=False)

Will order hotspots at the component level in descending order based on the complexity and the number of changes (see score column).

Exclude massive changesets

age_report = cm.AgeReport('.')
log = age_report.get_log()
threshold = int(log[['revision', 'path']].groupby('revision').
                sum().quantile(.99))
massive = get_massive_changesets(log, threshold)
log_ex_massive = log[~log['revision'].isin(massive['revision'])]

Will exclude changesets with a number of path changed in excess of the 99% percentile.

License

Licensed under the term of MIT License. See attached file LICENSE.txt.