1. Week 3 Presentation Istehad Chowdhury CISC 864 Mining Software Engineering Data

2. Research Paper Who Should Fix This Bug? John Anvik, Lyndon Hiew and Gail C. Murphy Department of Computer Science University of British Columbia {janvik, lyndonh, murphy}@cs.ubc.ca

3. Problem with Open Bug Repository Overall, to cope with the surge of bugs in large open source projects. “Everyday, almost 300 bugs appear that need triaging. This is far too much for only the Mozilla programmers to handle.” Many bug reports are invalid or duplicate of another bug report Eclipse, 36% Every bug report should be triaged To check validity and duplicity To assign the bug to an appropriate developer

4. Problem cont.. Triager may not be sure whom to assign the bug. Lot of time is wasted in reassigning and regaining 24% reports in Eclipse are re-assigned

5. The research work Goal: suggest whom to assign this bug to Technique: Using data mining and machine learning Result: 60% precision and 10% recall

6. Precision and Recall

7. Life Cycle of a Bug Report

8. Roles Reporter/Submitter Resolver Contributor Triager The roles are overlapping

10. Approach to the problem Semi automated 1. Characterizing bug reports 2. Assigning a label to each report 3. Choosing reports to train the supervised machine learning algorithm 4. Applying the algorithm to create the classifier for recommending assignments.

11. Heuristics on labeling bug reports FIXED (who provided last approved patch), Firefox FIXED (whoever marked report as resolved), Eclipse DUPLICATE: whoever resolved the report is duplicate. Eclipse and Firefox WORKSFORME (Firefox) -- unclassifiable.

12. Experimental Results Fig. Recommender accuracy and recall

13. Validating Results with GCC Why so poor result? Why recall is low in all cases, esp. gcc? Shows need of similarity in project natures.

14. Trying Alternatives

15. Trying Alternatives cont.. Unsupervised Machine learning Incremental Machine learning Incorporating Additional sources of Data Component based classifier

17. Points to Ponder

18. Points to Ponder cont.. Are new developers assigned any bug? “Needs further study to context of which it can be applied”-empirical research

19. Points to Ponder cont.. Was there enough instances to evaluate using Cross Validation? For firefox 75%, gcc 86% developers have less than 100 reports Why was the labeling mechanism more successful in case of gcc and Eclipse than firefox? 1% for Eclipse, 47% for firefox

20. Points in favor The research work was very intense Thoroughly studied Honest in identifying the limitations and smart pointing out of the future works It opens up interesting doors of future research

21. Points Against The study may not be suitable for a environment where there is a frequent change in the active set of developers The findings are too project specific and works well on “actual bugs” reports

22. Points Against cont.. If there is any naivety in the heuristics it also propagates to the filtering process based on the heuristics to train the classifier. I liked the way included the lesson learned section. However, the authors should have explained in more details how the mappings were done.

23. Concluding Remarks It shows promise for improving the bug assignment problem for OSS “Coordination bug reports and CVS is challenging” The effort is worth praising Identifies need for further research

24. Questions and Comments?