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Quick Start Guide

Software Development Workflow Simulation Ready! 🎉

What's Set Up

✅ Python 3.13 virtual environment
✅ Jupyter Lab with full data science stack
✅ SimPy for discrete event simulation
✅ Complete workflow simulation comparing development approaches

Start Jupyter Lab

# Method 1: Use the PowerShell script
.\start_jupyter.ps1

# Method 2: Manual activation
.\venv\Scripts\Activate.ps1
jupyter lab

# Method 3: Direct path
.\venv\Scripts\jupyter.exe lab

Current Server

🌐 Jupyter Lab is running at: http://localhost:8889/lab?token=10851cc20ad8c5a9d003d4f98516f673234c6e0876457050

Main Notebook

workflow_queue_simulation.ipynb - Complete software development workflow simulation

Simulation Scenarios

  • Traditional PR - Standard pull request workflow with 7 developers
  • AI-Enhanced PR - PR workflow with AI-assisted development (1.3x speed)
  • Pair Programming - Real-time collaborative development with 4 pairs
  • AI-Enhanced Pairs - Pair programming with AI assistance

Key Simulation Features

🔄 Realistic Modeling: Industry-based defect rates, rework cycles, context switching
📊 Comprehensive Analysis: Lead times, throughput, queue performance, statistical trends
⚡ Performance Insights: AI enhancement effects, pair programming benefits
🧮 Queue Theory: SimPy-based discrete event simulation with resource constraints

What You'll Learn

  • Lead Time Impact - How different workflows affect delivery speed
  • Bottleneck Analysis - Where queues form and impact performance
  • AI Benefits - Quantified improvements from AI-assisted development
  • Pair Programming ROI - Lower defect rates vs. reduced individual productivity
  • Context Switching Costs - Hidden overhead in traditional workflows

Next Steps

  1. Open Jupyter Lab in your browser
  2. Run the workflow_queue_simulation.ipynb notebook
  3. Analyze the generated charts and statistics
  4. Experiment with different parameters
  5. Use results for team discussions and process improvements

Happy simulating! 🚀📊