🤖 Update Benchmark Results

[thomhurst]

Automated Benchmark Update

This PR updates the benchmark documentation with the latest results from the Speed Comparison workflow.

Benchmarks Produced

Individual benchmark artifacts are available for download:

• benchmark-DataDrivenTests
• benchmark-AsyncTests
• benchmark-ScaleTests
• benchmark-MatrixTests
• benchmark-MassiveParallelTests
• benchmark-SetupTeardownTests
• benchmark-BuildTime
• benchmark-summary (aggregated overview)

Runtime Benchmarks

• AsyncTests
• DataDrivenTests
• MassiveParallelTests
• MatrixTests
• ScaleTests
• SetupTeardownTests

Build Benchmarks

• BuildTime
• 2025-11-17
• Ubuntu Latest • .NET SDK 10.0.100

Changes

• Updated benchmark data in docs/static/benchmarks/latest.json
• Updated historical trends in docs/static/benchmarks/historical.json
• Regenerated benchmark documentation in docs/docs/benchmarks/index.md
• Updated benchmark summary in docs/static/benchmarks/summary.json

Workflow Run

• Run ID: 19414471232
• Triggered: schedule
• Date:

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🤖 This PR was automatically created and will be merged automatically once CI checks pass.

[claude]

Claude finished @thomhurst's task —— View job

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Reviewing Automated Benchmark Update PR ✅

I've completed my review of this automated benchmark update PR. Here's my comprehensive feedback on code quality, potential issues, performance considerations, security concerns, and test coverage.

Todo List:

• Read repository guidelines (CLAUDE.md)
• Examine benchmark data structure and format
• Review markdown documentation generation
• Analyze automation workflow quality
• Check for security concerns in data handling
• Assess data validation and error handling
• Provide comprehensive feedback summary

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📋 Review Summary

This is a well-structured automated benchmark update system with consistent data handling. Overall quality is GOOD with some opportunities for improvement.

✅ Strengths

1. Code Quality & Structure

• Consistent JSON Schema: All benchmark data files follow a standardized structure with timestamps, environment metadata, and results
• Clean Separation of Concerns: Runtime vs. build benchmarks are properly categorized
• Automated Documentation: Markdown files are auto-generated with consistent formatting and visual charts (Mermaid)
• Environment Tracking: Proper capture of SDK versions, OS details, and benchmark tool versions

2. Performance Considerations

• Efficient Data Format: JSON structure is optimized for consumption by documentation site
• Parallel Benchmark Execution: Workflow uses matrix strategy for concurrent benchmark runs
• Artifact Caching: Build artifacts are shared between benchmark runs to avoid redundant compilation
• AOT Testing: Includes separate AOT (Ahead-of-Time) compilation benchmarks for TUnit

3. Automation Quality

• Robust Workflow Design: /home/runner/work/TUnit/TUnit/.github/workflows/speed-comparison.yml:1-199

  • Daily scheduled runs via cron (0 0 * * *)
  • Manual trigger support via workflow_dispatch
  • Proper concurrency controls to prevent overlapping runs
  • Environment-specific execution (Production vs Pull Request)

• Data Processing Pipeline: /home/runner/work/TUnit/TUnit/.github/scripts/process-benchmarks.js:1-149

  • Structured parsing of BenchmarkDotNet output
  • Environment metadata extraction
  • Consistent file naming and organization

⚠️ Areas for Improvement

1. Data Validation & Error Handling

• Missing Input Validation: The Node.js processing script lacks validation for malformed benchmark data
• No Schema Validation: JSON files aren't validated against a defined schema
• Silent Failure Handling: Some parsing errors may be swallowed without proper logging

Recommendation: Add JSON schema validation and error boundaries in the processing script.

2. Security Considerations

• Minimal Concerns: This is primarily data-only changes with no code execution
• Token Usage: Uses ADMIN_TOKEN for automated commits (appropriate for this use case)
• Artifact Handling: All artifacts are from internal CI runs, no external data sources

Recommendation: Consider adding integrity checks for benchmark data to detect anomalies.

3. Test Coverage

• No Unit Tests: The benchmark processing script lacks automated tests
• No Data Validation Tests: Historical data consistency isn't verified
• No Regression Detection: No automated alerts for significant performance regressions

Recommendation: Add unit tests for the benchmark processing logic and regression detection.

🔧 Technical Observations

Data Structure Quality /home/runner/work/TUnit/TUnit/docs/static/benchmarks/latest.json:1-50

{
  "timestamp": "2025-11-17T00:29:04.104Z",  // ✅ ISO format
  "environment": { /* detailed metadata */ }, // ✅ Comprehensive
  "categories": { /* organized by test type */ } // ✅ Well-structured
}

Documentation Generation /home/runner/work/TUnit/TUnit/docs/docs/benchmarks/AsyncTests.md:1-50

• ✅ Consistent markdown structure
• ✅ Visual charts with Mermaid
• ✅ Tabular data presentation
• ✅ Environment metadata included

Historical Tracking /home/runner/work/TUnit/TUnit/docs/static/benchmarks/historical.json:1-30

• ⚠️ Simple date tracking (could include more metadata)
• ✅ Chronological organization

📊 Performance Analysis

The benchmark results show excellent performance characteristics:

• TUnit AOT: 124.2ms (4.6x faster than regular TUnit)
• TUnit Regular: 572.1ms (18% faster than nearest competitor)
• Competitors: NUnit (696.5ms), MSTest (666.7ms), xUnit3 (742.0ms)

🛡️ Security Assessment

LOW RISK - This PR contains only documentation and data updates:

• No executable code changes
• All data originates from internal CI benchmarks
• Uses standard GitHub Actions security patterns

📈 Recommendations for Future Enhancements

1. Add schema validation for JSON files
2. Implement regression alerts when performance drops significantly
3. Add unit tests for the benchmark processing pipeline
4. Include confidence intervals in benchmark data
5. Add benchmark result comparison between runs
6. Implement data retention policies for historical data

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🎯 Verdict

APPROVED ✅ - This is a solid automated benchmark system that provides valuable performance insights for the TUnit project. The automation is well-designed, data is consistently structured, and the documentation generation is professional. The suggested improvements are minor enhancements that would make the system even more robust.

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