1. Knowledge Collaboration by
Mining Software Repositories
Tom Zimmermann
Saarland University, Saarbrücken, Germany

2. Guiding developers
Zimmermann, Weissgerber, Diehl, Zeller (TSE 2005)

5. eROSE suggests further locations.

7. eROSE prevents incomplete changes.

8. eROSE is customizable.

9. “Indirect” collaboration
Direct collaboration
Version
archive

10. “Indirect” collaboration
Direct collaboration
Version
archive
Mining
Hidden
Knowledge

11. “Indirect” collaboration
Direct collaboration
Indirect
Version
collaboration
archive
Mining
Hidden
Knowledge

12. Future

13. #1: Change classification

14. #1: Change classification
bad changes (e.g., from bug database)
X X X X

15. #1: Change classification
BUILD A CLASSIFIER
bad changes (e.g., from bug database)
X X X X

16. #1: Change classification
BUILD A CLASSIFIER
bad changes (e.g., from bug database)
X X X X
new change

17. #1: Change classification
BUILD A CLASSIFIER
bad changes (e.g., from bug database)
X X X X
new change
PREDICT QUALITY

18. #2: What should we collect
• Mining software repositories relied on
exiting repositories so far.
• Collecting new data (e.g., navigation traces)
opens new opportunities.
• Software(ICSM 2005), DeLine et al. (VL/HCC 2005)
Navigation
Singer et al
• Socialet al. (TagSea tool)
Tagging
Storey

19. Mining across projects

20. #3: Mining across projects
• Extend source code search engines with
mining techniques.
• Large scale mining (129,167 SF projects) and
large scale collaboration (1,393,250 SF users).
• Usage Pei (MSR 2006) Koders.com
patterns from
Xie and

21. Conclusion
• History supports knowledge collaboration.
• Future challenges: granularity and data.
• Mining software repositories @ ASE 2006:
− Wednesday 4pm: Impact analysis
− Friday 9am: Management
− Friday 11am: Mining software repositories