CI/CD Test Suite Optimization Using Monte Carlo Simulation

Practical Implementation for Real-World Pipelines

What This Notebook Does:

Ingests your test history and code change data
Calculates risk scores from failure rates, code churn, and test categories
Runs 10,000 Monte Carlo simulations to find optimal test selection
Outputs production-ready optimized test suite for CI/CD

Expected Results: 30-50% reduction in CI/CD time with maintained or improved bug detection

The Problem

Running all tests in CI/CD takes too long. Developers wait 20-30 minutes for feedback. But which tests can you safely skip?

The Solution: Monte Carlo Simulation

Instead of guessing which tests to run, we use statistical simulation:

Step 1: Analyze 30 days of test history - which tests fail most?
Step 2: Analyze git commits - which code changes most?
Step 3: Calculate risk scores for each test
Step 4: Run 10,000 simulated test selections weighted by risk
Step 5: Tests that appear in 70%+ of simulations = your optimized suite

What You Get

            Exported Files Ready for CI/CD:
            optimized_test_suite.json - Programmatic access
optimized_pytest_config.txt - pytest integration
github_actions_config.yml - GitHub Actions workflow
optimized_test_suite.csv - Spreadsheet analysis

            
            Results from Sample Data:
            Reduced 100 tests → 30-40 tests
Time: 25 minutes → 10 minutes (40% faster)
Risk coverage: 80%+
All high-risk modules covered

        

How to Use

Download the notebook (button above)
Replace sample data with your JUnit XML, pytest results, or git log
Run all cells - takes 2-3 minutes
Review optimized suite - add any critical tests manually
Export to CI/CD - use provided config files
Monitor and adjust - re-run monthly as code evolves

Technical Details

Monte Carlo Simulation: 10,000 iterations of weighted random test selection

Risk Scoring Factors:

Historical failure rate (40 points max)
Code churn of related module (30 points)
Test category priority (20 points)
Recent activity (10 points)

Selection Strategy: Tests appearing in 70%+ of simulations form the optimized suite

Expected Business Impact

Faster Feedback: Results in 10 minutes vs 20-30 minutes
Cost Savings: 40% reduction in CI/CD compute costs
Better Quality: Risk-based selection catches critical bugs first
Team Productivity: Less waiting, more coding

Download & Start Optimizing