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