The document summarizes a seminar presentation on a research paper about improving code review. The paper proposes two models: one to predict whether a code patch will be accepted or rejected, and another to recommend reviewers for a patch. The models were trained on data from Mozilla code reviews and achieved accurate predictions that could help reduce code review time by providing early feedback and directing patches to the best reviewers. The presentation covered the problem motivation, related work, the tool's approach and evaluation results showing high prediction accuracy.

1. Seminar on tool paper:
“Improving Code Review by Predicting
Reviewers and Acceptance of Patches ”
by
Gaeul Jeong, Sunghun Kim, Thomas
Zimmermann, and Kwangkeun Yi
Presented by Kishor Datta Gupta
Wed 14 Dec 2017

2. Problem
Code review time increasing
Reason:
It is manual process
Solutions:
• Predicting the review outcome
• Recommending reviewers

3. Background
Empirical analysis of the code review process
1. Why code review done manually?
2. Why reviewer assignments important?
Predicting review outcomes
1. Reviewers will get suggestions
2. Developer can get early feedback
Recommending patch reviewers
1. Suggesting a reviewer
2. Direct developer go to best reviewer

4. Background: Review Process
Figure 1 from the paper

5. Background: System
• FireFox
• Mozilla Core projects
Average review time 1.5 days
Acceptance rate 39% and 45%
Use Bugzilla System

6. Background: System Study
• 56000 code reviewed for study
Figure 1 from the paper

7. Background: System Study
• 56000 code reviewed for study
Figure2 from the paper

8. Background: System Study
• 56000 code reviewed for study
Figure 3 from the paper

9. Background: Study Result
• Developers have to manually select reviewer.
• Reviewers have to manually inspect the patch
• Without reviewers recommendation patch rejection
rate is higher

10. Tool: model
• Patch acceptance prediction model
• Reviewer prediction model

11. Tools: Approach
• Developer writes a patch
• Optionally runs our patch acceptance prediction
model
• Posts the patch with reviewer prediction model
• Reviewer reuse patch acceptance prediction for
faster decision

12. Tools: Patch Content

13. Tools: Bug Report

14. Evaluation method: Prediction Model
• Predicting an accepted patch as accepted (a -> a)
• Predicting an accepted patch as rejected (a -> r)
• Predicting a rejected patch as rejected (r -> r)
• Predicting a rejected patch as accepted (r -> a).

15. Evaluation method: Reviewer select
• Predicts the top N suitable reviewers.
• Correct if real reviewer exit in the list.

16. Evaluation results
Prediction result:

17. Evaluation Result
• Reviewer prediction
Figure4 from the paper

18. Evaluation Result
Figure 6 from the paper

19. Discussion
• How does the model work
• which features are important

20. Discussion
Figure 9 from the paper

21. Conclusion
• Claims:
-Predicting the review outcome.
-Recommending patch reviewers.
-Reduce the time spent in development