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Data Analytics Project Evaluation using Github Metrics as Key Performance Indicators (KPIs)

Instructions

  1. Clone the repository
  2. Add the csv file with all github project repo URLs in data/ folder
  3. Run code/update_repos.py to clone/pull latest changes in each repository
  4. Run code/get_stats.py to print all metrics on terminal and store results in output/ folder

Metrics

Overall Indicators

total number of:

  • commits
  • issues

Per Student Indicators

  1. % of commits project contribution distribution how well did they use version control

  2. Use of Version Control lesser % commits -> check LOC. if more, poor usage (bulk edits), if less, good usage (granular) more % of commits -> check LOC. if more, poor usage (bulk edits), if less, good usage (granular)

  3. commit frequency shows consistency of contribution

  4. % of issues assigned how well were the tasks distributed amongst members

  5. average time spent per issue - lead time time between when issue was opened and closed lesser lead time, better is the member's efficiency

  6. total time spent on all issues - coding hours total contribution in terms of time

  7. average importance of an issue - code churn It is the percentage of a developer’s own code representing an edit to their own recent work. LOC - modified, added and deleted number of files edited

PS

  • ignore changes in dataset files (.csv etc)
  • Files matching MIME type image, binary should be ignored