SESSION

A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI

A shorter pull request (PR) cycle time is essential for improving developer experience, but too often, pull requests are too complex, touch too many files, and require too many iterations to be quickly and thoroughly reviewed by a peer. The analysis of our data at Atlassian indicates that this results in longer PR cycles and release time.

In this session, we’ll introduce the PR Complexity Score, how we calculate it, and how it helps identify PRs that should be reworked before being submitted for review.

We’ll share how we make its value prominent and explain its meaning within the context of a pull request as part of a recent project we ran. Taking it a step further, we will illustrate how AI can assist by suggesting ways to simplify the changes.

Achieving faster approval for PRs is possible, and optimizing release time will be beneficial for everyone!

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PRESENTED BY
  • Company Logo

    Caterina Curti

    Senior Developer Advocate @ Atlassian

  • Company Logo

    Chris Williams

    Senior Software Engineer @ Atlassian

A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI Watch Video

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Develocity

Take it further with Develocity

Caterina and Chris share their research on how large and complex pull requests impact feature delivery by increasing cycle times and failure rates. Learn how Gradle customers use Develocity to speed up builds and tests, empowering developers to do smaller change sets (eg. smaller and less complex PRs) and ultimately ship features faster. Learn more about Develocity’s acceleration technologies enable faster release schedules.

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The slides

A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 1A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 2A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 3A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 4A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 5A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 6A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 7A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 8A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 9A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 10A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 11A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 12A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 13A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 14A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 15A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 16A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 17A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 18A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 19A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 20A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 21A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 22A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 23A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 24A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 25A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 26A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 27A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 28A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 29A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 30A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 31A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 32A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 33A Score for Pull Request Complexity – Its Impact on Cycle Time and How We Reduced it with AI — slide 34

Meet the speakers

  • Company Logo

    Caterina Curti

    Caterina is a Developer Advocate at Atlassian, focused on helping developers build apps that add functionalities to Atlassian products. When she is not working, you can find her admiring the Australian ocean and beaches or playing with a dog.

    LinkedIn
  • Company Logo

    Chris Williams

    Chris is a Senior Software Engineer at Atlassian, working on developing the fundamentals of the app ecosystem that expands functionality of various Atlassian products such as Jira and Confluence. He takes an active interest in development best practices and their effects on productivity and reliability.

    LinkedIn