Enterprise-ready Gh and Dyn libraries - 4

Automated version management with Release Drafter

Manual version numbering becomes impractical when managing releases across six software versions and multiple frameworks. Release Drafter automates semantic versioning based on pull request labels, integrating seamlessly with our build infrastructure:

# Configure release naming and tagging based on semantic versioning
name-template: '$RESOLVED_VERSION'
tag-template: '$RESOLVED_VERSION'

# Automatically determine version increments based on PR labels
version-resolver:
  major:
    labels: ['major']        # Breaking changes
  minor:
    labels: ['minor']        # New features, backward compatible
  patch:
    labels: ['patch']        # Bug fixes, patches

Developers apply semantic labels to pull requests. Release Drafter calculates version increments automatically and generates structured release notes. This integrates seamlessly with our build infrastructure, ensuring consistent versioning across all platform configurations.

Multi-module version management

The monolithic project structure we established in the first article creates multiple interdependent modules within a single repository:

• LibraryCore: Shared functionality used by all platforms
• LibraryDynamo: Dynamo-specific components (Extensions, Functions, UI)
• LibraryGrasshopper: Grasshopper-specific components and utilities

Rather than versioning the entire repository for every change, enterprise workflows require selective versioning based on which modules actually changed. This prevents unnecessary builds and maintains clear change tracking.

The four pathways to automated integration testing

When it comes to automating tests that touch Rhino + Grasshopper (and by extension, similar challenges exist for Dynamo), there are four mainstream approaches. Each bootstraps the host application in a different place, so their trade-offs revolve around where the application lives, how licenses are consumed, and how portable the workflow is to CI.

Path-based change detection

GitHub Actions path filtering enables selective module versioning:

name: Detect Changes
jobs:
  changes:
    runs-on: ubuntu-latest
    steps:
      - name: Filter changed projects
	uses: dorny/paths-filter@v2
	with:
	  filters: |
	    core: 'Core/**'           # Core library changes
	    dynamo: 'DYN/**'          # Dynamo components
	    grasshopper: 'GH/**'      # Grasshopper components
            
      # Additional workflow steps for processing changes
      - name: 📘 Create release
        if: steps.changes.outputs.code == 'true'  # Only run if code changed
        uses: release-drafter/release-drafter@v6
        id: release
        with:
          config-name: release-drafter.yml
          # Generate alpha versions for non-main branch builds
          prerelease: ${{ boolean switch }}
          prerelease-identifier: 'alpha'
          publish: true
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

This workflow only triggers versioning for modules with actual changes, reducing build overhead while the conditional compilation system handles platform-specific references and dependencies.

Recommended GitFlow workflow for GH/DYN

• Create two primary branches: Main and Develop
• Feature development: Create feature branches from Develop
• Testing and integration: Merge completed features back into Develop
  • Send alpha versions to testers for validation across platform configurations
• Production release: Merge from Develop to Main and deploy to users
  • Tag commits as releases for easier visualisation and tracking
• Hotfix management: Create hotfix branches from Main for critical production issues
  • Fix bugs and commit back to Main, then deploy immediately
  • Integrate hotfixes into active development branches to maintain consistency

This approach integrates with Release Drafter automation: each merged pull request updates draft releases with appropriate version increments, enabling coordinated releases across the entire platform matrix.

AI-enhanced version control workflows

The development landscape is evolving toward intelligent automation, where AI agents handle routine tasks while humans focus on strategic decisions. Current AI capabilities and near-future potential are transforming version control from manual oversight to intelligent collaboration.

Current AI capabilities

Modern AI agents can already automate several version control tasks:

• Pull Request Analysis: AI reviews code changes and suggests appropriate semantic version labels based on actual impact across platforms
• Dependency Detection: Automated analysis identifies when changes affect module dependencies or create conflicts in multi-platform builds
• Test Coverage Validation: AI ensures new changes include appropriate tests for supported platforms before allowing merges

Near-future agentic systems

Emerging agentic workflows represent a fundamental shift toward autonomous, multi-step processes. These AI systems operate through iterative loops – analysing data, making decisions, and executing actions without constant human intervention. Applied to version control, agentic workflows could:

• Predictive Impact Analysis: Multi-agent systems that analyse code changes, cross-reference internal repositories, and predict deployment impacts across the entire configuration matrix
• Automated Release Coordination: AI agents specialising in different aspects (dependency mapping, change impact prediction, release coordination) collaborate to reduce errors in multi-platform deployments
• Adaptive Version Management: Intelligent systems that understand project context, adapt to changing requirements, and make autonomous decisions about version increments based on semantic rules and historical patterns

Practical implementation

Early implementations already show promise. AI agents can analyse vast amounts of code changes in minutes instead of hours, cross-reference module dependencies, and produce customised impact reports. This mirrors successful applications in other fields where AI agents handle due diligence tasks that previously required days of manual work.

The competitive landscape suggests that the first organisations to integrate AI deeply into version control tools will achieve significant efficiency advantages. However, successful implementation requires addressing the stop-start reality of AEC development, where AI agents must adapt to irregular development cycles and maintain consistency across long-lived branches.

These agentic workflows complement rather than replace the sophisticated infrastructure we’ve built, enabling development teams to maintain multi-platform consistency while reducing manual overhead and improving decision quality in complex deployment scenarios.