Automating Order Tracking: From Functionality to Continuous Integration

Over the past few days I’ve been evolving order-tracking , a simple Spring Boot project originally created as a portfolio piece to showcase my technical skills. The idea behind it is not only to demonstrate how to build a clean and functional service — with REST endpoints, hexagonal architecture, persistence, and auditability — but also how to approach software engineering with modern practices.

This includes automation, reproducibility, and workflows that resemble those found in production environments, turning the project into a practical example of end-to-end software delivery.

What’s new?

This iteration has been all about automation, reproducibility, and developer experience. Here are the main areas I worked on:

1. GitHub Workflows for CI/CD

I introduced a set of workflows that now run automatically on every change:

• Build Workflow (build.yml )
  Ensures the project compiles, runs tests, and produces a valid artifact. By relying on the included mvnw wrapper, contributors don’t need Maven installed — builds are consistent regardless of local setup.

• Formatting Workflow (format.yml )
  Automatically checks code formatting and fails if the style guide isn’t followed. This removes endless discussions about tabs, spaces or import ordering — the workflow enforces the rules.

• Docker Workflow (docker.yml )
  Builds and publishes Docker images using the provided Dockerfile . The app can now be run in a container anywhere with a single command, which makes testing, deployment and demoing much easier.

• Release Workflow (release.yml )
  Automates tagging and packaging new versions, so releases are predictable and reproducible.

• Main → Develop Sync Workflow (sync-main-to-develop.yml )
  Keeps development branches aligned with the main branch, reducing drift and avoiding nasty merge surprises later.

2. Database seeding

I added seeding mechanisms to populate the database with initial data. This makes it much easier to spin up the application locally or in Docker and immediately have meaningful entities to work with. It also simplifies testing and demos — you don’t start with an empty database anymore.

3. Better documentation

The README was expanded with detailed instructions, a clearer explanation of the architecture, and a roadmap with ideas for the future (advanced observability, horizontal scaling, etc.).

Current state of the project

With these improvements, order-tracking is no longer just a local demo service:

• Reproducible builds thanks to mvnw and CI workflows.
• Consistent formatting enforced automatically on every push.
• Portable runtime via Docker, so the app can run anywhere.
• Seeded database for easier onboarding, testing and demos.
• Automated releases with predictable tags and artifacts.
• Updated documentation that helps anyone understand, run and extend the project.

What’s next?

On the roadmap:

• More integration tests with real infrastructure (databases, queues).
• Enable horizontal scaling with distributed messaging and event-driven design.
• Advanced observability: tracing, dashboards, metrics.
• Stronger security: authentication, authorization and secret handling.
• Automatic deployments to staging environments through GitHub Actions.

Closing thoughts

The most interesting part of this cycle wasn’t writing new business logic — it was wrapping the project in automation and reproducibility. Workflows now guard quality, Docker ensures portability, seeding provides a smoother developer experience, and releases happen in a controlled, repeatable way.

The project feels closer to something production-ready, and every improvement in automation has already paid for itself in confidence and speed.