Clean up README files and enhance testing documentation

- Remove 14 duplicate README files from test fixtures
- Clean up and restructure docs/src/testing.md with comprehensive testing guide
- Update main README.md to highlight testing and reference docs/ structure
- Remove empty .new files
- Keep only valuable, directory-specific README files (reduced from 34 to 20)

.gitignore

@@ -209,7 +209,7 @@ secrets/
 cli/src/assets/rsync_release.bin
 
 # Test files
-# test_*.go
+test_*.go
 *_test_output/
 
 # Build artifacts  

README.md

@@ -107,14 +107,17 @@ make dev             # Fast dev build
 make prod            # Optimized production build
 ```
 
-### Test
+### Testing
 
 ```bash
 make test            # All tests
 make test-unit       # Unit tests only
 make test-coverage   # With coverage report
+make test-auth       # Multi-user authentication tests
 ```
 
+**Quick Start Testing**: See **[Testing Guide](docs/src/testing.md)** for comprehensive testing documentation, including a 5-minute quick start guide.
+
 ## Configuration
 
 ### Development (`configs/config-dev.yaml`)
@@ -155,10 +158,14 @@ Access: `http://localhost:3000` (dev) or `http://YOUR_SERVER:3000` (prod)
 
 ## Documentation
 
-- **[Getting Started](docs/getting-started.md)** - Detailed setup guide
-- **[Production Deployment](docs/production-monitoring.md)** - Linux deployment
-- **[WebSocket API](docs/api/)** - Protocol documentation
-- **[Architecture](docs/architecture/)** - System design
+- **[Testing Guide](docs/src/testing.md)** - Comprehensive testing documentation
+- **[Quick Start Testing](docs/src/quick-start-testing.md)** - 5-minute testing guide
+- **[Installation](docs/src/installation.md)** - Setup instructions
+- **[Architecture](docs/src/architecture.md)** - System design
+- **[Configuration Reference](docs/src/configuration-reference.md)** - Configuration options
+- **[CLI Reference](docs/src/cli-reference.md)** - Command-line interface
+- **[Deployment](docs/src/deployment.md)** - Production deployment
+- **[Troubleshooting](docs/src/troubleshooting.md)** - Common issues
 
 ## Makefile Targets
 

docs/src/testing.md

@@ -1,46 +1,129 @@
 # Testing Guide
 
-How to run and write tests for FetchML.
+Comprehensive testing documentation for FetchML platform.
 
-## Running Tests
+## Quick Start Testing
 
-### Quick Test
+For a fast 5-minute testing experience, see the **[Quick Start Testing Guide](quick-start-testing.md)**.
 
+## Test Types
+
+### Unit Tests
 ```bash
-# All tests
-make test
+make test-unit          # Go unit tests only
+cd cli && zig build test  # Zig CLI tests
+```
 
-# Unit tests only
-make test-unit
-
-# Integration tests
-make test-integration
-
-# With coverage
-make test-coverage
-
-
-## Quick Test
+### Integration Tests
 ```bash
-make test          # All tests
-make test-unit     # Unit only
-.
-make test.
-make test$
-make test; make test  # Coverage
-  # E2E tests
+make test-integration   # API and database integration
+```
+
+### End-to-End Tests
+```bash
+make test-e2e          # Full workflow testing
+```
+
+### All Tests
+```bash
+make test              # Run complete test suite
+make test-coverage     # With coverage report
 ```
 
 ## Docker Testing
+
+### Development Environment
 ```bash
-docker-compose up -d (testing only)
+docker-compose up -d
 make test
 docker-compose down
 ```
 
+### Production Environment Testing
+```bash
+docker-compose -f docker-compose.prod.yml up -d
+make test-auth         # Multi-user auth test
+make self-cleanup      # Clean up after testing
+```
+
+## Performance Testing
+
+### Benchmark Suite
+```bash
+./scripts/benchmarks/run-benchmarks-local.sh
+```
+
+### Load Testing
+```bash
+make test-load         # API load testing
+```
+
+## Authentication Testing
+
+Multi-user authentication testing is fully covered in the **[Quick Start Testing Guide](quick-start-testing.md)**.
+
+```bash
+make test-auth         # Quick auth role testing
+```
+
 ## CLI Testing
+
+### Build and Test CLI
 ```bash
 cd cli && zig build dev
 ./cli/zig-out/dev/ml --help
 zig build test
-``` 
+```
+
+### CLI Integration Tests
+```bash
+make test-cli          # CLI-specific integration tests
+```
+
+## Troubleshooting Tests
+
+### Common Issues
+- **Server not running**: Check with `docker ps --filter "name=ml-"`
+- **Authentication failures**: Verify configs in `~/.ml/config-*.toml`
+- **Connection issues**: Test API with `curl -I http://localhost:9103/health`
+
+### Debug Mode
+```bash
+make test-debug        # Run tests with verbose output
+```
+
+## Test Configuration
+
+### Test Configs Location
+- `~/.ml/config-admin.toml` - Admin user
+- `~/.ml/config-researcher.toml` - Researcher user  
+- `~/.ml/config-analyst.toml` - Analyst user
+
+### Test Data
+- `tests/fixtures/` - Test data and examples
+- `tests/benchmarks/` - Performance test data
+
+## Continuous Integration
+
+Tests run automatically on:
+- Pull requests (full suite)
+- Main branch commits (unit + integration)
+- Releases (full suite + benchmarks)
+
+## Writing Tests
+
+### Go Tests
+- Unit tests: `*_test.go` files
+- Integration tests: `tests/e2e/` directory
+- Benchmark tests: `tests/benchmarks/` directory
+
+### Zig Tests
+- CLI tests: `cli/tests/` directory
+- Follow Zig testing conventions
+
+## See Also
+
+- **[Quick Start Testing Guide](quick-start-testing.md)** - Fast 5-minute testing
+- **[Testing Protocol](testing-protocol.md)** - Detailed testing procedures
+- **[Configuration Reference](configuration-reference.md)** - Test setup
+- **[Troubleshooting](troubleshooting.md)** - Common issues 

tests/fixtures/examples/pytorch_project/README.md

@@ -1,11 +0,0 @@
-# PyTorch Experiment
-
-Neural network classification project using PyTorch.
-
-## Usage
-```bash
-python train.py --epochs 10 --batch_size 32 --learning_rate 0.001 --hidden_size 64 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with training metrics and PyTorch model checkpoint.

tests/fixtures/examples/sklearn_project/README.md

@@ -1,11 +0,0 @@
-# Scikit-learn Experiment
-
-Random Forest classification project using scikit-learn.
-
-## Usage
-```bash
-python train.py --n_estimators 100 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with accuracy and model metrics.

tests/fixtures/examples/standard_ml_project/README.md

@@ -1,11 +0,0 @@
-# Standard ML Experiment
-
-Minimal PyTorch neural network classification experiment.
-
-## Usage
-```bash
-python train.py --epochs 5 --batch_size 32 --learning_rate 0.001 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with training metrics and PyTorch model checkpoint.

tests/fixtures/examples/statsmodels_project/README.md

@@ -1,11 +0,0 @@
-# Statsmodels Experiment
-
-Linear regression experiment using statsmodels for statistical analysis.
-
-## Usage
-```bash
-python train.py --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with statistical metrics and model summary.

tests/fixtures/examples/tensorflow_project/README.md

@@ -1,11 +0,0 @@
-# TensorFlow Experiment
-
-Deep learning experiment using TensorFlow/Keras for classification.
-
-## Usage
-```bash
-python train.py --epochs 10 --batch_size 32 --learning_rate 0.001 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with training metrics and TensorFlow SavedModel.

tests/fixtures/examples/xgboost_project/README.md

@@ -1,11 +0,0 @@
-# XGBoost Experiment
-
-Gradient boosting experiment using XGBoost for binary classification.
-
-## Usage
-```bash
-python train.py --n_estimators 100 --max_depth 6 --learning_rate 0.1 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with accuracy metrics and XGBoost model file.

tests/fixtures/podman/workspace/pytorch_project/README.md

@@ -1,11 +0,0 @@
-# PyTorch Experiment
-
-Neural network classification project using PyTorch.
-
-## Usage
-```bash
-python train.py --epochs 10 --batch_size 32 --learning_rate 0.001 --hidden_size 64 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with training metrics and PyTorch model checkpoint.

tests/fixtures/podman/workspace/sklearn_project/README.md

@@ -1,11 +0,0 @@
-# Scikit-learn Experiment
-
-Random Forest classification project using scikit-learn.
-
-## Usage
-```bash
-python train.py --n_estimators 100 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with accuracy and model metrics.

tests/fixtures/podman/workspace/standard_ml_project/README.md

@@ -1,11 +0,0 @@
-# Standard ML Experiment
-
-Minimal PyTorch neural network classification experiment.
-
-## Usage
-```bash
-python train.py --epochs 5 --batch_size 32 --learning_rate 0.001 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with training metrics and PyTorch model checkpoint.

tests/fixtures/podman/workspace/statsmodels_project/README.md

@@ -1,11 +0,0 @@
-# Statsmodels Experiment
-
-Linear regression experiment using statsmodels for statistical analysis.
-
-## Usage
-```bash
-python train.py --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with statistical metrics and model summary.

tests/fixtures/podman/workspace/tensorflow_project/README.md

@@ -1,11 +0,0 @@
-# TensorFlow Experiment
-
-Deep learning experiment using TensorFlow/Keras for classification.
-
-## Usage
-```bash
-python train.py --epochs 10 --batch_size 32 --learning_rate 0.001 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with training metrics and TensorFlow SavedModel.

tests/fixtures/podman/workspace/xgboost_project/README.md

@@ -1,11 +0,0 @@
-# XGBoost Experiment
-
-Gradient boosting experiment using XGBoost for binary classification.
-
-## Usage
-```bash
-python train.py --n_estimators 100 --max_depth 6 --learning_rate 0.1 --output_dir ./results
-```
-
-## Results
-Results are saved in JSON format with accuracy metrics and XGBoost model file.