# Benchkit Development Roadmap
- **Project:** benchkit
- **Version Target:** 1.0.0
- **Date:** 2025-08-08
- **Status:** ACTIVE
## Project Vision
Benchkit is a **toolkit, not a framework** for practical benchmarking with markdown-first reporting. It provides flexible building blocks that developers can combine to create custom benchmarking solutions tailored to their specific needs.
## Architecture Principles
- **Toolkit over Framework**: Provide composable functions rather than monolithic workflows
- **Markdown-First Reporting**: Treat markdown as first-class output format
- **Zero-Copy Where Possible**: Minimize allocations during measurement
- **Statistical Rigor**: Provide proper statistical analysis with confidence intervals
## Development Phases
### Phase 1: Core Functionality (MVP) - **Current Phase**
**Timeline:** Week 1-2
**Justification:** Essential for any benchmarking work
#### Core Features
- [x] **Basic Timing & Measurement** (`enabled` feature)
- Simple timing functions for arbitrary code blocks
- Nested timing for hierarchical analysis
- Statistical measures (mean, median, min, max, percentiles)
- Custom metrics support beyond timing
- [x] **Markdown Report Generation** (`markdown_reports` feature)
- Generate markdown tables and sections for benchmark results
- Update specific sections of existing markdown files
- Preserve non-benchmark content when updating documents
- [x] **Standard Data Generators** (`data_generators` feature)
- Lists of varying sizes (small: 10, medium: 100, large: 1000, huge: 10000)
- Maps with configurable key-value distributions
- Strings with controlled length and character sets
- Consistent seeding for reproducible benchmarks
#### Success Criteria
- [ ] New users can run first benchmark in <5 minutes
- [ ] Integration requires <10 lines of code
- [ ] Measurement overhead <1% for operations >1ms
- [ ] All core features work independently
#### Deliverables
1. **Project Structure**
- Cargo.toml with proper feature flags
- lib.rs with mod_interface pattern
- Core modules: timing, generators, reports
2. **Core APIs**
- `BenchmarkSuite` for organizing benchmarks
- `bench_block` for timing arbitrary code
- `MetricCollector` for extensible metrics
- `generate_list_data`, `generate_map_data` generators
3. **Testing Infrastructure**
- Comprehensive test suite in `tests/` directory
- Test matrix covering all core functionality
- Integration tests with real markdown files
### Phase 2: Analysis Tools
**Timeline:** Week 3-4
**Justification:** Needed for optimization decision-making
#### Features
- [ ] **Comparative Analysis** (`comparative_analysis` feature)
- Before/after performance comparisons
- A/B testing capabilities for algorithm variants
- Comparative reports highlighting differences
- [ ] **Statistical Analysis** (`statistical_analysis` feature)
- Standard statistical measures for benchmark results
- Outlier detection and confidence intervals
- Multiple sampling strategies
- [ ] **Baseline Management**
- Save and compare against performance baselines
- Automatic regression detection
- Percentage improvement/degradation calculations
#### Success Criteria
- [ ] Performance regressions detected within 1% accuracy
- [ ] Statistical confidence intervals provided
- [ ] Comparative reports show clear optimization guidance
### Phase 3: Advanced Features
**Timeline:** Week 5-6
**Justification:** Nice-to-have for comprehensive analysis
#### Features
- [ ] **HTML Reports** (`html_reports` feature)
- HTML report generation with customizable templates
- Chart and visualization embedding
- Interactive performance dashboards
- [ ] **JSON Reports** (`json_reports` feature)
- Machine-readable JSON output format
- API integration support
- Custom data processing pipelines
- [ ] **Criterion Compatibility** (`criterion_compat` feature)
- Compatibility layer with existing criterion benchmarks
- Migration tools from criterion to benchkit
- Hybrid usage patterns
- [ ] **Optimization Hints** (`optimization_hints` feature)
- Analyze results to suggest optimization opportunities
- Identify performance scaling characteristics
- Actionable recommendations based on measurement patterns
#### Success Criteria
- [ ] Compatible with existing criterion benchmarks
- [ ] Multiple output formats work seamlessly
- [ ] Optimization hints provide actionable guidance
### Phase 4: Ecosystem Integration
**Timeline:** Week 7-8
**Justification:** Long-term adoption and CI/CD integration
#### Features
- [ ] **CI/CD Tooling**
- Automated performance monitoring in CI pipelines
- Performance regression alerts
- Integration with GitHub Actions, GitLab CI
- [ ] **IDE Integration**
- Editor extensions for VS Code, IntelliJ
- Inline performance annotations
- Real-time benchmark execution
- [ ] **Monitoring & Alerting**
- Long-term performance trend tracking
- Performance degradation notifications
- Historical performance analysis
## Technical Requirements
### Feature Flag Architecture
| `enabled` | Core benchmarking functionality | ✓ | - |
| `markdown_reports` | Markdown report generation | ✓ | pulldown-cmark |
| `data_generators` | Common data generation patterns | ✓ | rand |
| `criterion_compat` | Compatibility layer with criterion | ✓ | criterion |
| `html_reports` | HTML report generation | - | tera |
| `json_reports` | JSON report output | - | serde_json |
| `statistical_analysis` | Advanced statistical analysis | - | statistical |
| `comparative_analysis` | A/B testing and comparisons | - | - |
| `optimization_hints` | Performance optimization suggestions | - | statistical_analysis |
### Non-Functional Requirements
1. **Performance**
- Measurement overhead <1% for operations >1ms
- Data generation must not significantly impact timing
- Report generation <10 seconds for typical suites
2. **Usability**
- Integration requires <10 lines of code
- Sensible defaults for common scenarios
- Incremental adoption alongside existing tools
3. **Reliability**
- Consistent results across runs (±5% variance)
- Deterministic seeding for reproducible data
- Statistical confidence measures for system noise
4. **Compatibility**
- Primary: std environments
- Secondary: no_std compatibility for core timing
- Platforms: Linux, macOS, Windows
## Implementation Strategy
### Development Principles
1. **Test-Driven Development**
- Write tests before implementation
- Test matrix for comprehensive coverage
- Integration tests with real use cases
2. **Incremental Implementation**
- Complete one feature before starting next
- Each feature must work independently
- Regular verification against success criteria
3. **Documentation-Driven**
- Update documentation with each feature
- Real examples in all documentation
- Performance characteristics documented
### Code Organization
```
benchkit/
├── Cargo.toml # Feature flags and dependencies
├── src/
│ ├── lib.rs # Public API and mod_interface
│ ├── timing/ # Core timing and measurement
│ ├── generators/ # Data generation utilities
│ ├── reports/ # Output format generation
│ └── analysis/ # Statistical and comparative analysis
├── tests/ # All tests (no tests in src/)
│ ├── timing_tests.rs
│ ├── generators_tests.rs
│ ├── reports_tests.rs
│ └── integration_tests.rs
├── benchmarks/ # Internal performance benchmarks
└── examples/ # Usage demonstrations
```
## Integration Patterns
### Pattern 1: Inline Benchmarking
```rust
use benchkit::prelude::*;
fn benchmark_my_function()
{
let mut suite = BenchmarkSuite::new("my_function_performance");
suite.benchmark("small_input", || {
let data = generate_list_data(10);
bench_block(|| my_function(&data))
});
suite.generate_markdown_report("performance.md", "## Performance Results");
}
```
### Pattern 2: Comparative Analysis
```rust
use benchkit::prelude::*;
fn compare_algorithms()
{
let comparison = ComparativeAnalysis::new()
.algorithm("original", || original_algorithm(&data))
.algorithm("optimized", || optimized_algorithm(&data))
.with_data_sizes(&[10, 100, 1000, 10000]);
let report = comparison.run_comparison();
report.update_markdown_section("README.md", "## Algorithm Comparison");
}
```
## Risk Mitigation
### Technical Risks
1. **Measurement Accuracy**
- Risk: System noise affecting benchmark reliability
- Mitigation: Statistical analysis, multiple sampling, outlier detection
2. **Integration Complexity**
- Risk: Difficult integration with existing projects
- Mitigation: Simple APIs, comprehensive examples, incremental adoption
3. **Performance Overhead**
- Risk: Benchmarking tools slowing down measurements
- Mitigation: Zero-copy design, minimal allocations, performance testing
### Project Risks
1. **Feature Creep**
- Risk: Adding too many features, losing focus
- Mitigation: Strict phase-based development, clear success criteria
2. **User Adoption**
- Risk: Users preferring existing tools (criterion)
- Mitigation: Compatibility layer, clear value proposition, migration tools
## Success Metrics
### User Experience Metrics
- [ ] Time to first benchmark: <5 minutes
- [ ] Integration effort: <10 lines of code
- [ ] Documentation automation: Zero manual copying
- [ ] Regression detection accuracy: >99%
### Technical Metrics
- [ ] Measurement overhead: <1%
- [ ] Feature independence: 100%
- [ ] Platform compatibility: Linux, macOS, Windows
- [ ] Memory efficiency: O(n) scaling with data size
## Next Actions
1. **Immediate (This Week)**
- Set up project structure with Cargo.toml
- Implement core timing module
- Create basic data generators
- Set up testing infrastructure
2. **Short-term (Next 2 Weeks)**
- Complete Phase 1 MVP implementation
- Comprehensive test coverage
- Basic markdown report generation
- Documentation and examples
3. **Medium-term (Month 2)**
- Phase 2 analysis tools
- Statistical rigor improvements
- Comparative analysis features
- Performance optimization
## References
- **spec.md** - Complete functional requirements and technical specifications
- **usage.md** - Lessons learned from unilang/strs_tools benchmarking
- **Design Rulebook** - Architectural principles and development procedures
- **Codestyle Rulebook** - Code formatting and structural patterns