Personal Developer Analytics

Master's thesis project — a self-hosted platform that aggregates developer productivity metrics from local Git repositories, GitHub, GitLab, and Jira, and surfaces them through a React dashboard with AI-generated insights via local Ollama.

Architecture

Layer	Technology
Backend API	Spring Boot 3.5, Java 17, JWT auth
Database	PostgreSQL 16, Flyway migrations
Frontend SPA	React 18, TypeScript, Vite, Tailwind v4, Recharts
AI insights	Ollama (llama3.2) — runs locally, no cloud dependency
Deployment	Docker Compose (app + Postgres + Ollama)

Prerequisites

• Java 17+
• Node 20+
• PostgreSQL 16 (or Docker)
• Ollama — for AI features
• openssl — for generating secrets
• Docker + Docker Compose — for one-command deployment

Environment Variables

Variable	Purpose	Default	How to generate
JWT_SECRET	JWT signing key	dev key (insecure)	openssl rand -hex 32
ENCRYPTION_KEY	AES-256-GCM token encryption key (base64)	dev key (insecure)	openssl rand -base64 32
POSTGRES_PASSWORD	Database password	123 (dev only)	choose one
SMTP_HOST	SMTP server for password-reset emails	smtp.gmail.com	—
SMTP_PORT	SMTP port	587	—
SMTP_USERNAME	SMTP sender address	—	your email
SMTP_PASSWORD	SMTP app password	—	your app password
OLLAMA_BASE_URL	Ollama server URL	http://localhost:11434	—
COOKIE_SECURE	Set true in production (HTTPS only)	false	—

In production, always set JWT_SECRET, ENCRYPTION_KEY, and POSTGRES_PASSWORD via environment variables or a secrets manager. Never commit real secrets.

Running Locally (Dev Mode)

# 1. Start Postgres
cd dev-analytics && docker compose up -d db

# 2. Start Ollama (separate terminal)
ollama serve
# First time only — downloads ~2 GB:
ollama pull llama3.2

# 3. Start the backend  http://localhost:8080
cd dev-analytics
./mvnw spring-boot:run

# 4. Start the frontend dev server  http://localhost:5173
cd dev-analytics/frontend
npm install
npm run dev

The frontend dev server proxies /api requests to localhost:8080.

One-Command Deployment (Docker Compose)

# 1. Copy and fill in secrets
cp .env.example .env
# Edit .env — set JWT_SECRET, ENCRYPTION_KEY, POSTGRES_PASSWORD

# 2. Start the full stack
cd dev-analytics && docker compose up -d

The app is available at http://localhost:8080.
First boot pulls the llama3.2 model (~2 GB) — allow a few minutes before the AI features work.

Building the Fat JAR

cd dev-analytics
./mvnw clean package      # builds frontend, embeds it in the JAR
java -jar target/dev-analytics-*.jar

Running Tests

# Backend unit + integration tests
cd dev-analytics && ./mvnw test

# Frontend tests
cd dev-analytics/frontend && npm run test:run

# Lint
cd dev-analytics/frontend && npm run lint

Demo Mode

Start with a pre-seeded dashboard — no real data source needed:

cd dev-analytics
./mvnw spring-boot:run --spring.profiles.active=demo

Login: demo@demo.com / demo

On first boot the seeder creates 12 weeks of realistic metrics. Subsequent boots skip seeding (idempotent).