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Generative AI for Pull Request Descriptions: Ad...

Tao Xiao
July 24, 2024

Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions

The slides for the paper "Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions" at FSE24

Tao Xiao

July 24, 2024
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  1. Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer

    Interventions FSE 24, Brazil Tao Xiao Hideaki Hata Christoph Treude Kenichi Matsumoto
  2. 4 Impact of Copilot for PRs on code review Merge

    rate Does it expedite reviews? Expedite Reviews Is there a higher chance of PRs being merged due to its input?
  3. 5 Early adoption of Copilot for PRs Developers can modify

    AI-generated content to meet their needs
  4. 6 Goal: Early adoption of Copilot for PRs and its

    impact on code review (RQ1) To what extent do developers use Copilot for PRs in the code review process? Adoption Trend of Copilot for PRs Proportions of Copilot for PRs Distribution of Copilot commands
  5. (RQ2) How are the code reviews affected by the use

    of Copilot for PRs? 7 Goal: Early adoption of Copilot for PRs and its impact on code review Is there a relationship between the use of Copilot for PRs and review time? Is there a relationship between the use of Copilot for PRs and the likelihood of a PR being merged?
  6. 8 Goal: Early adoption of Copilot for PRs and its

    impact on code review (RQ3) How do developers adapt the content suggested by Copilot for PRs? What kind of supplementary information complements the content suggested by Copilot for PRs? What kind of content suggested by Copilot for PRs undergoes subsequent editing by developers?
  7. 9 Goal: Early adoption of Copilot for PRs and its

    impact on code review (RQ3) How do developers adapt the content suggested by Copilot for PRs? (RQ1) To what extent do developers use Copilot for PRs in the code review process? (RQ2) How are the code reviews affected by the use of Copilot for PRs?
  8. 10 (RQ1) To what extent do developers use Copilot for

    PRs Proportions of Copilot for PRs P R s D e p e n d e n c e Experience Repositories with long experience periods of Copilot for PRs is also dependent on it
  9. 11 (RQ1) To what extent do developers use Copilot for

    PRs Proportions of Copilot for PRs Rely on pull_request_template.md PRs
  10. 12 (RQ2) How are the code reviews affected by the

    use of Copilot for PRs? Expedite Reviews? Merge rate? 17,177 closed/merged 50,695 closed/merged
  11. 14 (RQ2) How are the code reviews affected by the

    use of Copilot for PRs? Is there a relationship between the use of Copilot for PRs and the likelihood of a PR being merged? PRs (Copilot for PRs) are 1.57 times more likely to be merged than others
  12. Is there a relationship between the use of Copilot for

    PRs and review time? 15 PRs (Copilot for PRs) reduce 19.3 hours of review time (RQ2) How are the code reviews affected by the use of Copilot for PRs?
  13. 16 (RQ3) How do developers adapt the content suggested by

    Copilot for PRs? What kind of supplementary information complements the content suggested by Copilot for PRs? 22.7% 22.8% Associated links Static template Information
  14. 17 What kind of supplementary information complements the content suggested

    by Copilot for PRs? 12.8% Pull Request Intent 9.2% Testing Information 7.3% Visual Representation (RQ3) How do developers adapt the content suggested by Copilot for PRs?
  15. 18 What kind of content suggested by Copilot for PRs

    undergoes subsequent editing by developers? 22.9% Deletion (RQ3) How do developers adapt the content suggested by Copilot for PRs?
  16. 19 What kind of content suggested by Copilot for PRs

    undergoes subsequent editing by developers? 22.9% Deletion 20.8% Refinement (RQ3) How do developers adapt the content suggested by Copilot for PRs?
  17. 20 What kind of content suggested by Copilot for PRs

    undergoes subsequent editing by developers? 22.9% Deletion 20.8% Refinement 17.4% Exclusion (RQ3) How do developers adapt the content suggested by Copilot for PRs?
  18. 21 What kind of content suggested by Copilot for PRs

    undergoes subsequent editing by developers? 22.9% Deletion 20.8% Refinement 17.4% Exclusion 14.7% Replacement 12.3% Exchangement 6% Augmentation (RQ3) How do developers adapt the content suggested by Copilot for PRs?
  19. 23 Incorporate Copilot for PRs tags into PR templates Establish

    Copilot for PRs tag integration guidelines Recommendations
  20. 25 Generative AI for Pull Request Descriptions: Adoption, Impact, and

    Developer Interventions Our Paper! Replication Package! * Most icons and images in the slides were generated by ChatGPT or available at https://www.flaticon.com/.