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Best Practices?

Don't care

Structure

Build

Promotion

Binary Snapshots

CI via Ci Server

Local Build checks upstream

Local Build is fast

Quite a few jobs and build to manage

No local downstream check

  • Possibly many breakages
  • Feature Flow in Danger
  • Exploratory Learning Difficult

Latest is delayed

Branching

Good Feature Flow

Local Build is fast

Quite a few jobs and build to manage

No CI

Single Build

Simple

CI is build-in

Local CI

Exploratory Learning

IDE Mapping

VCS Performance

Build Performance

Release Management

Problems with Feature Flow

?

  • Separate Component Builds
  • Separate VCS Repos
  • Latest promoted
  • Build is downstream aware

Large Project Example

Common Patterns

Water Gates

Facts

Continuous Delivery with

Large Software Stacks

Q & A

The Good

The Bad?

  • 500 devs
  • Java & C++
  • 15 years old legacy base
  • 50 components
  • 20 team
  • Distributed
  • One EAR

Practice

The Bad?

The Good

Our Focus:

  • Build time 14-24 hours
  • Always manage to compile
  • 4 hours UI testing with frequent problems
  • No strong local build
  • Early Freeze
  • Conservative Changes

Component Based Enterprise Apps

Hans Dockter

CEO, Gradleware

Founder Gradle

hans.dockter@gradleware.com

The Bad?

The Good

L

(1 releasable unit)

Criteria

Size of Code Base

CI Component Build

CI Full Build

Pre-commit Build

Commit Frequency

Team Relationships

Team Size

Developer Quality

Code Quality

  • Builds promoted components
  • Much more stable
  • Good Feature Flow
  • Uses promoted upstream
  • Downstream aware
  • Deeper Testing
  • Promotes when successful
  • Uses promoted upstream
  • Downstream aware
  • Contract Testing
  • Needs to be fast

Reporting

Challenges

Performance

  • Parallel & Distributed
  • High level build avoidance
  • Extremely Important
  • Automation can't solve it all
  • Integration Problems
  • A statistical game
  • RCA for build breakages
  • Tests need to be good enough
  • Automated infrastructure setup
  • Potential resistance
  • Breaking Changes
  • Less CI pressure
  • Model Cooperation Specifics
  • Possibly many breakages
  • Feature Flow in Danger
  • Hard Exploration Learning

L

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