Knowledge Collaboration by Mining Software Repositories

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Knowledge Collaboration by Mining Software Repositories

  1. 1. Knowledge Collaboration by Mining Software Repositories Tom Zimmermann Saarland University, Saarbrücken, Germany
  2. 2. Guiding developers Zimmermann, Weissgerber, Diehl, Zeller (TSE 2005)
  3. 3. eROSE suggests further locations.
  4. 4. eROSE prevents incomplete changes.
  5. 5. eROSE is customizable.
  6. 6. “Indirect” collaboration Direct collaboration Version archive
  7. 7. “Indirect” collaboration Direct collaboration Version archive Mining Hidden Knowledge
  8. 8. “Indirect” collaboration Direct collaboration Indirect Version collaboration archive Mining Hidden Knowledge
  9. 9. Future
  10. 10. #1: Change classification
  11. 11. #1: Change classification bad changes (e.g., from bug database) X X X X
  12. 12. #1: Change classification BUILD A CLASSIFIER bad changes (e.g., from bug database) X X X X
  13. 13. #1: Change classification BUILD A CLASSIFIER bad changes (e.g., from bug database) X X X X new change
  14. 14. #1: Change classification BUILD A CLASSIFIER bad changes (e.g., from bug database) X X X X new change PREDICT QUALITY
  15. 15. #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
  16. 16. Mining across projects
  17. 17. #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
  18. 18. 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

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