Skip to content

TestingGuide.md

Latest commit

 

History

History
223 lines (156 loc) · 6.25 KB

TestingGuide.md

File metadata and controls

223 lines (156 loc) · 6.25 KB

Testing Guide

This document describes the testing approach for the BPF Performance project.

Overview

The BPF Performance project focuses on benchmarking and performance testing of BPF programs and maps. Our testing strategy ensures that:

  • Performance benchmarks are reliable and consistent
  • New features don't introduce performance regressions
  • The codebase remains maintainable and correct

Test Categories

Performance Tests

Performance tests are the core of this project. They measure the execution time and resource usage of various BPF operations.

Current test categories include:

  • Map Operations: Testing different map types (hash, array, LRU, etc.)
  • Program Execution: Benchmarking BPF program execution performance
  • Helper Functions: Testing the performance of BPF helper functions
  • Tail Calls: Measuring tail call performance and overhead

When adding new performance tests:

  • Use consistent measurement methodologies
  • Run tests multiple times and report statistical measures (mean, median, std dev)
  • Document expected performance characteristics
  • Include both small and large dataset tests where appropriate

Unit Tests

Unit tests verify the correctness of individual components:

  • Test harness functionality
  • Data processing and analysis code
  • Configuration parsing
  • Result formatting and output

Integration Tests

Integration tests verify that different components work together:

  • End-to-end test execution
  • Result collection and aggregation
  • Cross-platform compatibility
  • Different BPF runtime environments

Regression Tests

Regression tests ensure that performance doesn't degrade over time:

  • Automated comparison with baseline performance metrics
  • Detection of significant performance changes
  • Historical performance tracking

Test Structure

Test Organization

Tests are organized by functionality:

tests/
├── performance/          # Performance benchmark tests
│   ├── maps/            # Map performance tests
│   ├── programs/        # Program execution tests
│   └── helpers/         # Helper function tests
├── unit/                # Unit tests
├── integration/         # Integration tests
└── regression/          # Regression tests

Test Data

  • Use representative test data that reflects real-world usage
  • Include both synthetic and real-world datasets
  • Ensure test data is reproducible and deterministic
  • Document data characteristics and expected performance

Running Tests

Local Development

To run all tests locally:

# Build the project
cmake --build build

# Run performance tests
./build/bin/runner --test-suite performance

# Run specific test categories
./build/bin/runner --test-suite maps
./build/bin/runner --test-suite programs

Continuous Integration

All tests run automatically on:

  • Pull request creation and updates
  • Commits to the main branch
  • Scheduled daily runs for regression testing

Performance Baselines

  • Baseline performance metrics are maintained for each supported platform
  • Significant deviations from baselines trigger alerts
  • Baselines are updated when intentional performance changes are made

Adding New Tests

Performance Test Guidelines

When adding new performance tests:

  1. Define clear objectives: What performance aspect are you measuring?
  2. Use appropriate metrics: Choose metrics that matter to users
  3. Ensure reproducibility: Tests should produce consistent results
  4. Document expectations: Include expected performance ranges
  5. Consider platform differences: Account for hardware and OS variations

Test Implementation

// Example performance test structure
typedef struct test_config {
    const char* name;
    size_t iterations;
    size_t data_size;
    // Test-specific configuration
} test_config_t;

int run_performance_test(const test_config_t* config) {
    // Setup test environment

    // Warm-up runs

    // Measured runs
    for (size_t i = 0; i < config->iterations; i++) {
        start_timer();
        // Execute test operation
        end_timer();
    }

    // Calculate and report statistics

    // Cleanup
}

Test Documentation

Each test should include:

  • Clear description of what is being tested
  • Expected performance characteristics
  • Hardware/software requirements
  • Known limitations or variations

Performance Analysis

Metrics Collection

Standard metrics collected for each test:

  • Execution time: Mean, median, standard deviation
  • Throughput: Operations per second
  • Resource usage: CPU, memory utilization
  • Latency: Response time distribution

Statistical Analysis

  • Use appropriate statistical methods for performance comparison
  • Account for measurement noise and system variations
  • Report confidence intervals where applicable
  • Identify and handle outliers appropriately

Reporting

Test results are reported in standardized formats:

  • CSV files for raw data
  • JSON for structured results
  • Grafana dashboards for visualization
  • Summary reports for human consumption

Best Practices

Test Reliability

  • Run tests in isolated environments when possible
  • Use consistent test harnesses and measurement tools
  • Account for system noise and background processes
  • Validate test setup before measurement

Performance Regression Detection

  • Establish clear thresholds for performance changes
  • Use statistical tests to identify significant regressions
  • Automate regression detection in CI/CD pipelines
  • Provide clear feedback when regressions are detected

Cross-Platform Testing

  • Test on multiple operating systems and architectures
  • Account for platform-specific performance characteristics
  • Document platform differences and limitations
  • Use platform-appropriate optimization techniques

Troubleshooting

Common Issues

  • Inconsistent results: Check for background processes, thermal throttling
  • Test failures: Verify test environment setup and dependencies
  • Performance regressions: Compare with known baselines, check for configuration changes

Debugging Performance Issues

  • Use profiling tools to identify bottlenecks
  • Compare results across different environments
  • Check for resource contention or system limitations
  • Validate test methodology and measurement accuracy