This approach supports bug triaging on a multi-touch table to foster collaboration.

1. N/A
Katja Kevic, Sebastian C. Müller, Thomas Fritz, and Harald C. Gall
Collaborative Bug Triaging
CHASE „13, San Francisco – May 25, 2013

2. Motivation
How to support developers for collaborative bug triaging?
2
bug
bug
bug
bug bug
bug

3. Related Work
• Source code analysis [e.g. MCDonald 2000]
• «One out of four bug reports required
dicussion and negotiation..» [Carstensen,
1995]
3J. Anvik, L. Hiew, and G. C. Murphy, “Who should fix this bug?,” in Proceedings of the 28th International Conference on Software Engineering, ICSE ‟06.
D. W. McDonald and M. S. Ackerman, “Expertise recommender: a flexible recommendation system and architecture,” in Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, CSCW ‟00,
Carstensen, P. H., Sorensen, C. and Tuikka, T., Let's talk about bugs! Scandanavian Journal of Information Systems, 1995. 7,1 33-54.
• Information Retrieval or Machine Learning
[e.g. Anvik 2006]

4. Related Work
• Source code analysis [e.g. MCDonald 2000]
• «One out of four bug reports required
dicussion and negotiation..» [Carstensen,
1995]
4J. Anvik, L. Hiew, and G. C. Murphy, “Who should fix this bug?,” in Proceedings of the 28th International Conference on Software Engineering, ICSE ‟06.
D. W. McDonald and M. S. Ackerman, “Expertise recommender: a flexible recommendation system and architecture,” in Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, CSCW ‟00,
Carstensen, P. H., Sorensen, C. and Tuikka, T., Let's talk about bugs! Scandanavian Journal of Information Systems, 1995. 7,1 33-54.
• Information Retrieval or Machine Learning
[e.g. Anvik 2006]

5. Collaborative Bug
Triaging
Collaboration
IR + change set analysis
Allow change set investigation
5

6. Information Retrieval –
Finding similar Bugs
0.78
0.72
0.71
cosine similarity
threshold
> 0.7
6

7. Information Retrieval –
Finding similar Bugs
0.78
cosine similarity
threshold
7
> 0.75

8. Information Retrieval –
Finding similar Bugs
0.78
0.72
0.71
cosine similarity
threshold
8
> 0.6

9. Change Set Analysis –
Finding Potential Experts
0.71
0.78
0.72
5.46
1.44
4.28
9
Developer 1
Developer 2
Developer 3
7
Change set 1
2
Change set 2
2
Change set 3
4
Change set 4
Similar bug 1
Similar bug 2
Similar bug 3

10. Prototype: Analysis
10

11. Prototype: Context
11

12. Collaboration
12

13. Evaluation
• Applied in our own software projects
• Future work: user studies
13

14. Summary
14
Collaboration
IR + change set analysis
Allow change set investigation
For more details visit:
http://www.ifi.uzh.ch/seal/people/kevic/researchprojects/CollabBugTriaging.html

15. References
15
J. Anvik, L. Hiew, and G. C. Murphy, “Who should fix this bug?,” in
Proceedings of the 28th International Conference on Software Engineering,
ICSE ‟06, (New York, NY, USA), pp. 361–370, ACM, 2006.
D. W. McDonald and M. S. Ackerman, “Expertise recommender: a
flexible recommendation system and architecture,” in Proceedings of
the 2000 ACM Conference on Computer Supported Cooperative Work,
CSCW ‟00, (New York, NY, USA), pp. 231–240, ACM, 2000.
Carstensen, P. H., Sorensen, C. and Tuikka, T., Let's talk about
bugs! Scandanavian Journal of Information Systems, 1995. 7,1 33-54.