Code review prediction
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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.
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In recent years, data security has been one of the biggest concerns, and individuals have grown increasingly worried about the security of their personal information. Personalization typically necessitates the collection of individual data for analysis, exposing customers to privacy concerns. Companies create an illusion of safety to make people feel safe using a mainstream word, "encryption". Though encryption protects personal data from an external breach, the companies can still exploit personal data collected from users as they own the encryption keys. We present a naive yet secure approach for recommending movies to consumers without collecting any personally identifiable information. Our proposed approach can assist a movie recommendation system understand user preferences using the user's movie watch-time and watch history only. We conducted a comprehensive and comparative study on the performance of three deep reinforcement learning architectures, namely DQN, DDQN, and D3QN, on the same task. We observed that D3QN outperformed the other two architectures and achieved a precision of 0.880, recall of 0.805, and F1 score of 0.830. The results show that we can build a competitive movie recommendation system using unidentifiable data.
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Code review prediction
- 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
- 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.
- 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