collaborative bug triaging

N/A

Katja Kevic, Sebastian C. Müller, Thomas Fritz, and Harald C. Gall

Collaborative Bug Triaging

CHASE ‘13, San Francisco – May 25, 2013

Motivation

How to support developers for collaborative bug triaging?2

bug

bug

bug

bug bug

bug

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]

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]

Collaborative Bug Triaging

Collaboration

IR + change set analysis

Allow change set investigation

5

Information Retrieval – Finding

similar Bugs

0.78

0.72

0.71

cosine similarity

threshold

> 0.7

6

Information Retrieval – Finding

similar Bugs

0.78

cosine similarity

threshold

7

> 0.75

Information Retrieval – Finding

similar Bugs

0.78

0.72

0.71

cosine similarity

threshold

8

> 0.6

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

7Change set 1

2Change set 2

2Change set 3

4Change set 4

Similar bug 1

Similar bug 2

Similar bug 3

Prototype: Analysis

10

Prototype: Context

11

Collaboration

12

Evaluation

• Applied in our own software projects

• Future work: user studies

13

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

References

15

J. Anvik, L. Hiew, and G. C. Murphy, “Who should fix this bug?,” inProceedings 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: aflexible recommendation system and architecture,” in Proceedings ofthe 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.