Building reliable AI systems through rigorous testing and automation
15 Research Notebooks Organized in 3 Categories:
🟢 Practical Applications - Databricks Testing, AutoTriage Assessment, Healthcare AI Agents, CI/CD Optimization, RAG Testing, MCP Testing
🔵 Academic Research - I QA Workforce Transformation, AutoTriage Research, Multi-Agent Orchestration, Monte Carlo Testing, Model Evaluation, LLM Testing
🟡 Frameworks & Tools - Agentic Testing, Automated Patterns, AI Safety
graph TB
Research[AI Testing Research Portfolio]
Research --> Practical[Practical Applications]
Research --> Academic[Academic Research]
Research --> Tools[Frameworks & Tools]
Practical --> Databricks[Databricks Testing<br/>64% Time Reduction]
Practical --> AutoTriage[AutoTriage Assessment<br/>3.2x ROI]
Practical --> Healthcare[Healthcare AI Agents<br/>487% ROI]
Practical --> CICD[CI/CD Optimization<br/>40% Time Saved]
Practical --> RAG[RAG Testing<br/>Applications]
Practical --> MCP[MCP in Testing<br/>Context-Aware]
Academic --> IQA[I QA Transformation<br/>Workforce Forecasting]
Academic --> AutoTriageResearch[AutoTriage Research<br/>85% Accuracy]
Academic --> MultiAgent[Multi-Agent Orchestration<br/>80.2% Detection]
Academic --> MonteCarlo[Monte Carlo Testing<br/>POFOD Estimation]
Academic --> Evaluation[Model Evaluation<br/>GPT-4 vs Claude]
Academic --> LLMTest[LLM Testing<br/>Methodologies]
Tools --> Agentic[Agentic Testing<br/>Integration]
Tools --> Patterns[Automated Testing<br/>Patterns]
Tools --> Safety[AI Safety<br/>Metrics]
style Databricks fill:#51cf66,stroke:#2f9e44,color:#000
style AutoTriage fill:#51cf66,stroke:#2f9e44,color:#000
style Healthcare fill:#51cf66,stroke:#2f9e44,color:#000
style CICD fill:#51cf66,stroke:#2f9e44,color:#000
style IQA fill:#74c0fc,stroke:#1971c2,color:#000
style AutoTriageResearch fill:#74c0fc,stroke:#1971c2,color:#000
style MultiAgent fill:#51cf66,stroke:#2f9e44,color:#000
style MonteCarlo fill:#74c0fc,stroke:#1971c2,color:#000
style Evaluation fill:#74c0fc,stroke:#1971c2,color:#000
| Research Paper | Type | Key Results | Primary Focus | Tech Stack |
|---|---|---|---|---|
| I, QA: Workforce Transformation | Academic | 70-85% automation by 2028 | QA profession forecasting | Bass Diffusion, Monte Carlo |
| Databricks Testing Framework | Practical | 64% time ↓, $1.2M savings | Unified testing platform | Databricks, Delta Lake, MLflow |
| Healthcare AI Agents | Case Study | 487% ROI, 92% coverage | Why use AI agents? | LangChain, Playwright |
| AutoTriage Research Paper | Academic | 85% accuracy, 3.2x ROI | Test automation triage | Ensemble AI Framework |
| AutoTriage Assessment Tool | Tool | 4-tier prioritization | Manual test assessment | Business Value Analysis |
| CI/CD Test Optimization | Tool | 40% time reduction | Optimize pipeline | Monte Carlo, Python |
| Multi-Agent Orchestration | Academic | 80.2% detection, 31% cost ↓ | Optimal architecture | ATAO Framework |
| Monte Carlo Testing | Research | POFOD estimation | Statistical testing | Monte Carlo, scipy |
| Model Evaluation | Framework | GPT-4 vs Claude vs Gemini | Which AI model? | Python, pandas |
| Agentic Testing | Integration | Multi-agent systems | Implementation guide | AutoGPT, LangChain |
| MCP Testing | Framework | Context-aware testing | Dynamic adaptation | MCP Protocol |
| RAG Testing | Applications | Test generation from docs | Knowledge retrieval | RAG, Vector DBs |
| LLM Methodologies | Analysis | Hallucination detection | Testing LLMs | Safety frameworks |
| AI Safety Metrics | Metrics | Prompt injection detection | Security validation | Safety evaluators |
| Testing Patterns | Patterns | AI-augmented automation | Best practices | Pytest, CI/CD |
CI/CD Test Optimization Tool (Production-Ready)
Impact: 40% time reduction • 80% risk coverage
Focus: Ingests test history, runs 10,000 Monte Carlo simulations, outputs optimized suite
Exports: JSON, pytest, GitHub Actions
CI-CD monte-carlo test-optimization DevOps
View | Download
Healthcare AI Agents Case Study (Practical)
Impact: 487% ROI • 92% coverage • 88% faster tests
Focus: Why QA pros use AI agents - 7 agent types with HIPAA compliance
AI-agents healthcare-QA HIPAA-compliance autonomous-testing
View | Download
Multi-Agent Orchestration Framework (Academic)
Impact: 80.2% detection • 31% cost reduction • ANOVA validated
Focus: 4 architectures, 50 trials, statistical validation
multi-agent-systems test-orchestration manager-worker
View | Download
Monte Carlo Testing Framework (Research)
Impact: POFOD estimation • Statistical reliability assessment
Focus: Risk-based testing, fuzzing, chaos engineering
monte-carlo statistical-testing POFOD
View | Download
AI Model Evaluation Framework (Comparative)
Impact: Comprehensive model comparison
Focus: GPT-4 • Claude 3.5 • Gemini Pro • CodeLlama
AI-model-evaluation LLM-benchmarking GPT-4
View | Download
View All 15 Research Notebooks → | Complete Research Index
Transform your QA team in 6-12 months: 487% ROI • 40-70% efficiency gains • 85%+ automation coverage
32-week phased strategy for QA Directors and Engineering Leaders ready to lead the AI transformation.
Preview Framework → | Request Full Access → (Premium)
Advanced validation for Large Language Models with RAG, MCP, and safety testing
Impact: 23% accuracy improvement • 60% faster testing • 3 critical safety violations prevented
Tech: JavaScript/Node.js, AI APIs, RAG, MCP
LLM-testing AI-safety RAG MCP production-AI
Live Demo | Documentation | Case Studies
Gradual AI integration for enterprise systems without disruption
Impact: 40% faster processing • 60% fraud reduction • Zero downtime migration
Tech: Python, Legacy System Integration, AI/ML Pipeline
Framework Details | Assessment Tool
Ethical AI-powered automation for career management
Impact: 60% time reduction • 85% job matching accuracy • Improved application quality
Tech: Python, Playwright, AI/ML, React/TypeScript
Quick Start | Try Dashboard | Documentation
Interactive comparison of 10 AI-powered development environments
Analysis: 100+ hours testing • S-Tier through B-Tier rankings • Real-world performance insights
IDEs: Cursor, Windsurf, Void, Continue.dev, GitHub Copilot, Zed, Replit AI, CodeWhisperer, Tabnine
developer-tools AI-assistants IDE-comparison code-editors
View Comparison | Source Code
Systematic quantitative trading with risk management
Performance: +127% total return • 1.67 Sharpe ratio • 64% win rate
Tech: Python, pandas, Statistical Analysis, Risk Management
Strategy Details | Implementation
Production-ready Playwright automation validating this portfolio
8 reliable tests | 4x faster execution | Core Web Vitals monitoring | CI/CD integrated
Stack: Playwright • JavaScript • GitHub Actions
Coverage: Functional • Performance Testing
View Test Coverage & Metrics
| Category | Tests | Key Features |
|---|---|---|
| Functional | 5 | Homepage smoke test, social links, navigation, project links |
| Performance | 3 | Core Web Vitals (LCP, FCP, CLS, TTFB), page load, resource analysis |
Optimizations:
- Page Object Model architecture
- Parallel execution (4 workers locally, 2 in CI)
- Smart retry logic (1 local, 2 in CI)
- Test tagging (@smoke, @performance, @fast, @critical)
- Custom fixtures for reusability
CI/CD:
- Automated GitHub Actions workflow
- HTML report artifacts (30-day retention)
- Video & trace capture on failure
- Badge status in README
Quick Start:
npm install && npx playwright install --with-deps
npm run test:smoke # Fast smoke tests
npm run test:ui # Interactive modeDocumentation: Test Plan • Setup Guide • Test Suite Docs • Quick Reference
Test your skills at distinguishing AI-generated code from human-written code
Can you spot the difference between code written by AI and code written by humans? This interactive game presents real code snippets and challenges you to identify their origin. Learn the subtle patterns that distinguish AI coding style from human creativity and problem-solving approaches.
Features:
- 6 diverse code examples from simple functions to complex implementations
- Real-time scoring and accuracy tracking
- Educational explanations for each code snippet
- Mobile-responsive futuristic design
- No registration required - jump right in!
Challenge yourself: Can you achieve 80%+ accuracy and earn the "AI Code Detective" title?
- Projects Deployed: 5 production systems (including Portfolio Testing Suite)
- Performance Improvement: 23-60% across projects
- Testing Coverage: 85%+ automated validation
- Test Automation: 8 E2E tests, 4x faster execution, Core Web Vitals monitoring
- AI Frameworks: RAG, MCP, LLM testing, safety validation
Found this useful? Here's how you can help:
- Star the repo to show support
- Report issues you encounter
- Suggest improvements via issues
- Share with your network
- Issues: Join the conversation about AI-First development
- Issues: Report bugs or request features
- Contributors: See who's helping build this project
- QA Agentic Workflows Guide - Build your own specialized AI agents for daily QA work - Free solutions, Monday-Friday workflows, chat agents
- AI Advancements Q4 2025 - Major AI breakthroughs and their impact on quality engineering - GPT-5.2, Gemini 3.0, Agentic AI, Multimodal AI analysis
- QA-to-AI Transformation Roadmap - 🎯 Transform your QA team to AI-first in 6-12 months (487% ROI teaser available, 🔒 Full roadmap - Premium)
- Prompt Engineering Guide - Master effective AI prompting techniques
- AI Workflow Integration - Integrate AI into daily development workflows
- AI-First Principles - Core philosophy and development approach
- AI Adoption Roadmap - Step-by-step guide for teams adopting AI
New to AI-First development? Start here: START HERE Guide
Want to customize this template? See: Customization Guide
Unified autonomous agent system working on this portfolio 24/7
Last Updated: Loading...
| Component | Status | Last Run | Details |
|---|---|---|---|
| UAA Workflow | [UNKNOWN] Unknown | N/A | View Runs |
| CI-Fix Capability | [UNKNOWN] Unknown | N/A | View Status |
| Link-Health Capability | [UNKNOWN] Unknown | N/A | View Status |
| Security Capability | [UNKNOWN] Unknown | N/A | View Status |
- Dashboard will update after first UAA run
Dashboard auto-updated by UAA after each run
Status: [ACTIVE] Active | Architecture: Modular, Single Workflow, Multiple Capabilities
| Capability | Status | Purpose | Key Features | Links |
|---|---|---|---|---|
| CI-Fix | [ACTIVE] Active | Auto-fix CI/CD failures | Fixes npm sync, missing deps, creates issues for complex errors | Guide |
| Link-Health | [ACTIVE] Active | Prevent broken links | Weekly link scans, creates PRs with fix reports, alerts on critical links | Guide |
| Security | [ACTIVE] Active | Security monitoring | npm audit, secret detection, auto-fixes moderate issues, critical alerts | Guide |
Unified Workflow: .github/workflows/unified-autonomous-agent.yml
Architecture: Unified Agent Architecture | Agent README
Why Unified Architecture? Single point of maintenance • Shared utilities • Modular design • Easy to extend • Consistent logging
| Agent | Status | Purpose | Links |
|---|---|---|---|
| SEO-MA SEO Monitor Agent |
🔜 Planned | Monitor SEO health | Roadmap |
| PMA Performance Monitor Agent |
🔜 Planned | Track performance | Roadmap |
| DUA Dependency Update Agent |
🔜 Planned | Keep dependencies current | Roadmap |
| CUA Content Update Agent |
🔜 Planned | Maintain content freshness | Roadmap |
| AA Analytics Agent |
🔜 Planned | Generate insights | Roadmap |
Why Autonomous Agents? 24/7 operation • Instant response • Consistent quality • Demonstrates practical AI agentic workflows
Learn to build your own: QA Agentic Workflows Guide | Full Roadmap
├── tests/ # Portfolio Testing Project (QA Showcase)
│ ├── README.md # Complete test documentation
│ ├── QUICK_REFERENCE.md # Command reference card
│ ├── portfolio.spec.js # Smoke tests with POM
│ ├── navigation-links.spec.js # Link validation tests
│ ├── performance.spec.js # Core Web Vitals testing
│ ├── pages/ # Page Object Models
│ │ └── PortfolioPage.js # Portfolio POM
│ └── fixtures/ # Custom test fixtures
│ └── portfolio-fixtures.js # Reusable fixtures
├── playwright.config.js # Advanced Playwright config
├── TEST_PLAN.md # Comprehensive test plan (17 sections)
├── PLAYWRIGHT_SETUP_GUIDE.md # Setup and optimization guide
├── PLAYWRIGHT_OPTIMIZATIONS_SUMMARY.md # Detailed optimizations
├── .github/ # GitHub configuration
│ ├── workflows/ # CI/CD pipelines
│ │ └── playwright-tests.yml # Automated test workflow
│ └── PLAYWRIGHT_EMAIL_SETUP.md # Email reporting setup
├── llm-guardian/ # LLM Testing Framework (Flagship Project)
│ ├── README.md # Framework documentation
│ ├── demo.html # Interactive demonstrations
│ ├── index.html # Main entry point
│ ├── src/ # Core framework code
│ │ ├── evaluators/ # Testing evaluators
│ │ ├── llm-tester.js # Main testing interface
│ │ ├── rag-evaluator.js # RAG system evaluation
│ │ ├── safety-evaluator.js # Safety validation
│ │ └── mcp-server.js # MCP integration
│ ├── examples/ # Usage examples
│ │ └── demo.js # Demo implementations
│ ├── case-studies/ # Real-world implementations
│ │ ├── README.md
│ │ ├── financial-services-chatbot.md
│ │ └── ecommerce-recommendations.md
│ └── reasoning-examples/ # Extended thinking examples
│ └── test-planning-reasoning.md
├── legacy-ai-bridge/ # Enterprise AI integration framework
│ ├── README.md # Framework overview
│ └── assessment-template.md # Legacy system evaluation
├── job-search-automation/ # AI automation project
│ ├── README.md # Project documentation
│ ├── quick-start.html # Interactive setup guide
│ ├── app.html # Production dashboard
│ ├── backend/ # FastAPI backend
│ │ ├── main.py # API server
│ │ ├── job_scraper.py # Job board integration
│ │ ├── resume_parser.py # Resume parsing
│ │ └── job_matcher.py # AI matching engine
│ └── ethical-automation-guide.md
├── ai-ide-comparison/ # AI IDE comparison project
│ ├── index.html # Interactive comparison tool
│ └── README.md # Project documentation
├── algorithmic-trading/ # Quantitative trading project
│ ├── README.md # Strategy overview and results
│ └── strategy-implementation.md # Technical implementation
├── qa-prompts/ # AI prompt library for QA/SDET
│ ├── README.md # Library overview
│ ├── prompts/ # Categorized prompt collections
│ │ ├── test-generation.md
│ │ ├── api-testing.md
│ │ ├── code-generation.md
│ │ └── mobile-testing.md
│ └── examples/
│ └── sample-outputs.md
├── research/ # AI Research & Jupyter Notebooks
│ ├── index.html # Research landing page
│ ├── notebooks/ # Jupyter notebook collection
│ │ ├── README.md # Complete notebook index with tags
│ │ ├── ai-agents-qa-healthcare.ipynb # Healthcare AI agents case study
│ │ ├── ai-agents-qa-healthcare.html # HTML viewer
│ │ ├── model-evaluation-software-testing.ipynb # AI model evaluation framework
│ │ ├── model-evaluation-software-testing.html # HTML viewer
│ │ ├── agentic-testing-integration.ipynb # Agentic testing research
│ │ ├── agentic-testing-integration.html # HTML viewer
│ │ ├── mcp-software-testing.ipynb # MCP applications
│ │ ├── mcp-software-testing.html # HTML viewer
│ │ ├── rag-testing-applications.ipynb # RAG for testing
│ │ ├── rag-testing-applications.html # HTML viewer
│ │ ├── llm-testing-analysis.ipynb # LLM testing methodologies
│ │ ├── llm-testing-analysis.html # HTML viewer
│ │ ├── ai-safety-metrics.ipynb # AI safety metrics
│ │ ├── ai-safety-metrics.html # HTML viewer
│ │ ├── automated-testing-patterns.ipynb # Testing patterns
│ │ └── automated-testing-patterns.html # HTML viewer
│ └── papers/ # Research papers
│ ├── automated-testing-patterns.md
│ └── automated-testing-patterns.html
├── docs/ # Learning resources and guides
│ ├── PROMPT-ENGINEERING-GUIDE.md
│ ├── AI-WORKFLOW-INTEGRATION.md
│ ├── AI-FIRST-MANIFESTO.md
│ ├── AI-FIRST-PRINCIPLES.md
│ ├── AI-ADOPTION-ROADMAP.md
│ ├── START-HERE.md
│ ├── CUSTOMIZATION.md
│ ├── ARCHITECTURE.md
│ ├── FEATURES.md
│ ├── DEVELOPMENT-TIMELINE.md
│ └── SEO-AND-DISCOVERABILITY-GUIDE.md
├── learn/ # Interactive learning hub
│ ├── index.html # Learning portal
│ └── README.md
├── screenshots/ # Project screenshots
│ └── README.md
├── .github/ # GitHub configuration
│ └── workflows/ # CI/CD pipelines
├── images/ # Assets and media
│ ├── profile.jpg
│ ├── ela-mcb-metallic.jpg
│ ├── favicon.svg
│ └── site.webmanifest
├── index.html # Main portfolio page
├── analytics.html # Analytics dashboard
├── ANALYTICS-README.md # Analytics documentation
├── PROJECTS.md # Complete project list
├── CONTRIBUTING.md # Contribution guidelines
├── LICENSE # MIT License
└── README.md # This file
This portfolio demonstrates AI-First development practices using advanced AI systems:
- Rapid Prototyping: Complete portfolio architecture designed and implemented in 1-2 days instead of 2-3 weeks
- AI-Assisted Development: Leveraged multiple AI systems for code generation, optimization, and rapid iteration
- Human-AI Collaboration: Strategic decisions, domain expertise, and quality control maintained by human developer
- Efficiency Gains: ~10x faster development cycle through intelligent automation and AI pair programming
- Technical Partnership: Advanced AI systems as development accelerators and code generation partners
This project was built using AI-First development practices with:
- Cursor AI Agentic Mode - Advanced code generation and pair programming
- Void IDE - AI-powered development environment and workflow automation
- Claude 4 Sonnet - Architecture planning, documentation, and complex reasoning
- DeepSeek AI - Rapid iteration and optimization support
- DeepSeek Coder - Specialized code generation and technical implementation
Every technique in our guides was used to build this portfolio:
- Complete HTML/CSS generation with AI assistance for rapid iteration
- Advanced AI frameworks (RAG, MCP, LLM testing) implemented with AI assistance
- Production-ready CI/CD pipeline configured with AI guidance
Perfect for: Developers wanting to 10x their productivity, QA engineers transitioning to AI-first practices, and teams adopting AI-assisted development workflows.
MIT License - feel free to use this template for your own portfolio!
@portfolio{elamcb2025,
address = {USA},
author = {Elena Mereanu},
title = {{AI-First Quality Engineering Portfolio}},
url = {https://elamcb.github.io},
linkedin = {https://linkedin.com/in/elenamereanu},
github = {https://github.com/ElaMCB},
year = {2025}
}