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Setting Up Version Control

Step-by-Step Guide to Setting Up Version Control Integrations in DevDynamics

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Last updated 10 months ago

This guide will help you set up your version control integrations and configure them to measure all the metrics and insights generated by DevDynamics. We'll demonstrate the setup using GitHub, but the process is similar for other version control tools.

Step 1: Integrate Your Version Control System

  • Available Integrations: DevDynamics supports various version control tools, including: GitHub, GitLab, Azure Repos,Bitbucket, Custom Tools (configurable as needed)

  • Integration Process:

  • Integrate your version control system during onboarding.

  • After integration, you will see an Active Status on your screen. This indicates that DevDynamics is successfully pulling data from your version control system.

  • DevDynamics pulls data using APIs, only storing metadata and not copying your repositories.

Step 2: Configure Repositories

  • View Repository List:

  • You will see a list of all repositories in your version control system, with two main columns:

  • Active: A switch button to enable or disable tracking for specific repositories.

  • Settings: Additional configuration options for each repository.

  • Enable/Disable Tracking:

  • Use the switch button in the Active column to enable or disable tracking for specific repositories.

Step 3: Exclude Data

Excluding branch or PRs helps you focus on relevant data and avoid noise in your metrics.

  • Exclude branch - If your team work with feature branches you should add branches that are not a target to a direct commit and used only as a baseline branch (e.g. master, main, stage,dev )

  • Exclude Pull Request by title or Label - Certain open PR's affect your cycle time. If those PR does not represent the feature or bug change you can exclude them by title or label.

By following these steps, you can effectively set up and configure version control integrations in DevDynamics. This setup ensures that your version control data is accurately tracked and that any unnecessary data is excluded from your metrics, providing you with precise insights and measurements. This helps maintain a clean and relevant dataset for better analysis and decision-making.

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