This project simulates and analyzes different software development workflows to understand their impact on team productivity, lead times, and overall efficiency. Using SimPy discrete event simulation, it models real-world scenarios including traditional PR workflows, AI-enhanced development, and pair programming.
The simulation compares four development approaches:
- Traditional PR Workflow - Standard pull request review process
- AI-Enhanced PR - PR workflow with AI-assisted development
- Pair Programming - Real-time collaborative development
- AI-Enhanced Pairs - Pair programming with AI assistance
- Python 3.12+ in a virtual environment
- Jupyter Lab and Jupyter Notebook
- Simulation Libraries: SimPy for discrete event simulation
- Data Science Stack: NumPy, Pandas, Matplotlib, Seaborn
- Statistical Analysis: Comprehensive metrics and visualizations
cd "c:\Users\micha\source\repos\QueueSimulation"
.\venv\Scripts\Activate.ps1jupyter labjupyter notebookThe main notebook contains a complete software development workflow simulation with:
- Queue Theory Modeling - SimPy-based discrete event simulation
- Multiple Scenarios - Traditional PR, AI-enhanced PR, pair programming, and AI-enhanced pairs
- Realistic Parameters - Based on industry research and best practices
- Comprehensive Analysis - Lead time trends, throughput metrics, and statistical comparisons
- Professional Visualizations - Charts and graphs for presenting results
- Developer Defect Rates: 75% for individual work (traditional and AI-enhanced), 45% for pair programming
- AI Speed Enhancement: 1.3x coding speed improvement (no quality improvement)
- Realistic AI Assessment: AI provides speed benefits but introduces different defect types that negate quality gains
- Rework Cycles: Up to 3 cycles with diminishing defect rates
- Context Switching: 30-minute penalties for task switching
- Queue Dynamics: Realistic PR review bottlenecks
-
Traditional PR Workflow (7 developers)
- Individual development → PR submission → Review queue → Rework cycles
- One dedicated reviewer, queue-based bottlenecks
-
AI-Enhanced PR (7 developers)
- Same workflow with 1.3x coding speed
- Same defect rate as traditional (AI doesn't improve quality)
- Reduced development time, same review process
-
Pair Programming (4 pairs = 8 developers)
- Immediate commit with lower defect rates
- No PR queue, immediate feedback cycles
-
AI-Enhanced Pairs (4 pairs = 8 developers)
- Fastest coding with pair programming quality benefits
- AI provides speed, pairing provides quality improvement
- Combined benefits of AI speed and pair programming defect reduction
The notebook generates comprehensive analysis including:
- Lead Time Comparison - Average completion times across all scenarios
- Throughput Analysis - Total tickets completed per scenario
- Rework Statistics - Average rework cycles and patterns
- Queue Performance - PR review bottlenecks and wait times
- Trend Analysis - Daily metrics and performance evolution
- Statistical Significance - Distribution analysis and confidence intervals
# Activate environment
.\venv\Scripts\Activate.ps1
# Deactivate environment
deactivate
# Install additional Python packages
pip install package-name# Start Jupyter Lab (recommended)
jupyter lab
# Start classic Jupyter Notebook
jupyter notebook
# Install additional packages in the environment
pip install simpy matplotlib seaborn pandas numpyThe simulation reveals important findings about software development workflows:
- AI-Enhanced Pairs - Fastest delivery with lowest defect rates
- Pair Programming - Fast delivery with immediate feedback
- AI-Enhanced PR - Moderate improvement over traditional approaches
- Traditional PR - Longest lead times due to queue bottlenecks
- Queue Bottlenecks - PR review queues significantly impact lead times
- Context Switching - Task switching penalties add substantial overhead
- Defect Rates - Pair programming dramatically reduces defects (45% vs 75%)
- AI Enhancement - Consistent 30% speed improvement with no quality change (realistic assessment)
- Realistic AI Impact - AI introduces different defect types that offset speed benefits in terms of quality
# If activation fails, try:
Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
# Then activate again:
.\venv\Scripts\Activate.ps1# Upgrade pip:
python -m pip install --upgrade pip
# Install specific versions:
pip install simpy==4.0.1 pandas==1.5.0- Run the simulation to see current results
- Experiment with parameters - Adjust defect rates, team sizes, AI speed multipliers
- Add new scenarios - Model other development approaches
- Extend the analysis - Add cost analysis, quality metrics, or team satisfaction
- Create presentations - Use the generated charts for stakeholder discussions
- SimPy Environment - Discrete event simulation engine
- Resource Queues - PR review bottlenecks and capacity constraints
- Process Flows - Developer workflows with realistic timing
- Data Collection - Metrics tracking and statistical analysis
- Visualization - Professional charts and trend analysis
- Tickets - Work items with creation, completion, and rework tracking
- Developers - Configurable AI enhancement and pairing capabilities
- Scenarios - Different workflow implementations with team structures
- Metrics - Daily tracking and summary statistics
- SimPy Documentation - Discrete event simulation framework
- Queue Theory - Mathematical foundations
- Pair Programming Research - Academic studies on effectiveness
- DevOps Research - Industry benchmarks and metrics
Happy simulating! 🚀📊