SuperPrice

Project Overview

SuperPrice is a full-stack web application engineered by a team of 5 developers over 5 months. The platform tracks and compares grocery pricing data, helping users navigate fluctuating item pricing through visual analytics and historical price tracing.

The system is built around a decoupled architecture, isolating a strongly-typed, object-oriented backend service from an interactive, state-managed user interface.

Engineering Architecture

1. Robust Backend Architecture (Spring Boot)

The core backend service was built using Java and Spring Boot, adhering strictly to enterprise object-oriented programming (OOP) principles. We designed a layered controller-service-repository architecture to isolate business logic:

• Secure Authentication: Implemented structured JSON Web Token (JWT) request interceptors to enforce session management.
• Database Integration: Engineered relational schemas utilizing MySQL to map out cross-referenced tables for stores, product listings, user accounts, and real-time pricing histories.
• REST API Footprint: Designed and exposed 10+ decoupled endpoints allowing the client-side UI to fetch parameterized query data smoothly.

2. Interactive Client Interface (React & TypeScript)

The frontend was built using React and TypeScript to guarantee type safety across our data objects. We implemented global state handlers to manage user sessions, dynamic search filters, and an internal notification engine that updates pricing metrics dynamically.

Collaborative Workflow & Automation

Because this platform was built in a multi-developer environment, maintaining code quality and preventing integration conflicts across parallel branches was critical.

1. Git Workflow & Sprints

We managed our scope and feature deliverables using GitHub Projects, splitting our milestone requirements across engineering sprints. We adopted a strict feature-branch git workflow, ensuring that no code merged into the main development branch without undergoing peer code reviews via GitHub pull requests.

2. High-Coverage CI/CD Pipeline

To enforce system stability across parallel developments, I established an automated GitHub Actions workflow that triggered on every single pull request.

The pipeline automated the build execution step, ran environment validations, and pushed our server through automated test suites utilizing JUnit 5. By committing to strict verification tracking, we achieved an overall backend testing benchmark of 95%+ code coverage, completely eliminating regression bugs before merge phases.

3. Containerized Cloud Deployment

Once features passed integration validation check layers, the pipeline automatically containerized the services using Docker and published the system build images directly to AWS Elastic Container Registry (ECR). The application was then orchestrated and deployed on AWS Elastic Beanstalk infrastructure for live execution.