PDE Ecosystem Demo — Garth Puckerin

64 / 64

Tests Passing

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Python Versions CI

GDPR

Privacy-First

What it is

The PDE Ecosystem Demo is the production-stable, publicly deployed reference implementation of the Professional Development Ecosystem — a framework for enforcing enterprise-grade quality, security, and maintainability across any software project. Where the PDE / IPDE page describes the philosophy, this is the deployed artefact: a pip-installable Python package, a compiled VS Code extension (.vsix), a containerised Flask dashboard, and a CI pipeline that validates everything across four Python versions on every push.

The core premise: quality gates, privacy protection, and automated analysis should be infrastructure, not afterthoughts. PDE wires those primitives into a single ecosystem any project can adopt with one command — and the demo repo is the proof that it works.

System layers

Foundation Code Quality Agent — AST-based multi-language analysis (Python, TS, React, Vue, Angular) with auto-fix, serialiser validation, permission auditing, and migration checks.

Monitoring Ecosystem Dashboard — Flask + SocketIO real-time interface on port 5000. Tracks quality score trends, server health, Git status, and sprint task queues with one-click server management.

Privacy Privacy-First Architecture — GDPR/CCPA-compliant PrivacyQualityAgent with consent gating, 15+ sensitive data pattern detection (API keys, PII, credentials), Fernet encryption at rest, and right-to-delete. Local-only processing by default — code never leaves the machine.

Intelligence Memory System — Multi-layer persistence (Redis cache → ORM → file storage) with cross-project pattern learning, Fernet-encrypted sensitive data, and relevance-scored recommendations.

Editor VS Code Extension — TypeScript extension host + Python WebSocket backend. Live quality feedback per file, sidebar intelligence panel, status bar score indicator, and auto-detect project type on init.

Container Docker / Vercel — Production Dockerfile with non-root user, healthcheck on /api/health, and docker-compose orchestration. Demo slice deployed to Vercel for public access.

Automation GitHub Actions CI — Matrix pipeline across Python 3.8–3.11; installs Redis + PostgreSQL, validates CLI commands, runs pytest with coverage reporting (XML + HTML), builds the package, and uploads to Test PyPI.

Architecture

CLI Entry Point pde init / analyze / quality-check / dashboard ↓ Core Layer Quality Agent · PrivacyQualityAgent · Project Detector ↓ Intelligence Layer Memory System (Redis + ORM) · Pattern Scoring ↓ Transport WebSocket (8765) · REST API (5000) · VS Code RPC ↓ Dashboard Flask + SocketIO · Token auth admin API · /api/health ↓ Container Dockerfile · docker-compose · Vercel demo deployment ↓ CI / CD GitHub Actions matrix · Coverage XML + HTML · PyPI publish

Quality thresholds enforced

85%

Code Quality

80%

Test Coverage

90%

Performance

95%

Accessibility

100%

Security Gates

90%

Doc Coverage

Current CI test results

Total suite (64/64) 100%

Privacy integration (13/13) 100%

Dashboard security (4/4) 100%

Privacy detection coverage 89–90%

Overall code coverage 48%

Quick start

# Install and initialise in any project $ pip install pde-ecosystem $ pde init --project-type django --quality-threshold 90 # Run analysis with auto-fix $ pde analyze --fix # Launch real-time dashboard $ pde dashboard --host 0.0.0.0 --port 5000 # Full quality gate check $ pde quality-check

Key design decisions

Provider-agnostic stack detection: The project detector infers framework from file patterns, directory structure, and import signatures — no config required. The same CLI command works on a Django monolith and a React SPA.
Privacy as a layer, not a feature: PrivacyQualityAgent subclasses the base agent and gates every operation through explicit consent checks. Sensitive data is filtered before analysis using 15+ pattern detectors, file paths are anonymised in reports, and GDPR right-to-delete is a first-class API method.
Multi-layer memory with encryption: Quality patterns are persisted in Redis for speed, backed to the ORM for durability, and sensitive fields are Fernet-encrypted (AES-128 CBC, PBKDF2 100k iterations) at rest — so cross-project learning never leaks project-specific context.
GitHub Actions as the contract: The CI matrix (3.8–3.11, Redis, PostgreSQL) is the authoritative definition of "this works." Local dev is fast; the pipeline is the proof.
Extension-backend separation: The VS Code extension hosts only the TypeScript UI layer; all analysis runs in the Python backend over WebSocket. Swapping the editor surface leaves the analysis engine untouched.
Privacy module as first-class citizen: PII detection, anonymisation, consent management, and encryption live in a dedicated pde/privacy/ module — not bolted on after the fact.

Implementation phases

Complete Phase 1 — Foundation: Quality Agent, Dashboard, Memory System, CI pipeline

Active Phase 2 — Intelligence: Predictive analytics, adaptive thresholds, performance forecasting

Planned Phase 3 — Advanced Analytics: 3D health mapping, cross-project learning transfer

Planned Phase 4 — Enterprise: Multi-tenant, industry benchmarking, compliance monitoring

Planned Phase 5 — AI Integration: Natural language reports, LLM-powered recommendations

Planned Phase 6 — Deployment & Scaling: Distributed monitoring, production hardening

Relationship to PDE / IPDE

PDE / IPDE is the upstream vision — natural-language specs producing governed, reproducible code artefacts. The PDE Ecosystem Demo is what you run around that process: the quality infrastructure that validates, monitors, and persists everything PDE / IPDE generates. One converts intent to code; the other ensures that code meets the bar before it ships.