# Benchmark Implementation Audit

* **Task ID:** benchmark-implementation-audit_25.stevejs
* **Reviewer:** stevejs
* **Area:** code
* **Motivation (WHY):**
  - Need to verify what code differences exist between DFA, Manual FST, and Dialog State Machine benchmarks
  - Ensure relative performance comparisons are valid by confirming similar work is being measured
  - Identify any implementation differences that could skew performance comparisons

* **Acceptance Criteria:**
  1. **Code Path Analysis**: Document exactly what code each benchmark executes
  2. **Work Comparison**: Verify all benchmarks do equivalent sentence detection work
  3. **Overhead Identification**: Identify any benchmark-specific overhead differences
  4. **Implementation Equivalence**: Confirm benchmarks measure comparable functionality

* **Analysis Targets:**
  - **DFA Benchmark**: `dfa_detector.detect_sentences()` - what does this call?
  - **Manual FST Benchmark**: `manual_detector.detect_sentences()` - what does this call?  
  - **Dialog State Machine**: `DialogStateMachine.detect_sentences()` - what does this call?

* **Key Files:**
  - `/Users/stevejs/guten_split/benches/sentence_detector_bench.rs` (benchmark functions)
  - `/Users/stevejs/guten_split/src/sentence_detector.rs` (DFA and Manual FST implementations)
  - `/Users/stevejs/guten_split/tests/dialog_state_machine_exploration.rs` (Dialog State Machine implementation)

* **Investigation Points:**
  1. **Input Processing**: Do all three process the same input text format?
  2. **Core Algorithm**: What's the actual sentence detection logic in each?
  3. **Output Generation**: What format conversion/processing happens in each?
  4. **Memory Allocation**: Any differences in string handling, vector allocation?
  5. **Position Calculation**: How does each compute line:col positions?

* **Deliverables:**
  - **Code Path Document**: Step-by-step breakdown of what each benchmark executes
  - **Work Equivalence Analysis**: Comparison of actual sentence detection work performed
  - **Overhead Analysis**: Identification of benchmark-specific overhead in each approach
  - **Performance Validity Assessment**: Whether relative performance comparisons are meaningful

* **Method:**
  1. **Trace DFA benchmark**: Follow `SentenceDetectorDFA::detect_sentences()` implementation
  2. **Trace Manual FST benchmark**: Follow `SentenceDetector::detect_sentences()` implementation
  3. **Trace Dialog State Machine**: Follow `DialogStateMachine::detect_sentences()` implementation
  4. **Compare work performed**: Identify differences in actual computation vs overhead
  5. **Document findings**: Clear breakdown of what each benchmark measures

## Analysis Results

### Detection Statistics (Pride and Prejudice, 743,375 characters)
- **Manual FST**: 6,981 sentences (avg 103.1 chars)
- **DFA**: 5,238 sentences (avg 137.7 chars)  
- **Dialog State Machine**: 4,321 sentences (avg 169.0 chars)
- **Sentence count variance**: 80.6%

### Key Findings

**1. Non-Equivalent Work - Different Sentence Detection Logic**
The three implementations perform fundamentally different sentence detection:
- **Manual FST**: Complex character-by-character FST boundary validation
- **DFA**: Simple regex pattern `[.!?]\s+[A-Z]` 
- **Dialog State Machine**: Context-aware state machine with dialog boundaries

**2. Non-Equivalent Work - Different Processing Steps**
- **Manual FST & DFA**: Include O(n) `normalize_sentence()` processing per sentence
- **Dialog State Machine**: Skips normalization entirely
- This creates unfair performance comparison (extra O(n) + memory overhead)

**3. Implementation Quality Issues**
- **Dialog State Machine**: Has quote duplication bug at sentence boundaries
- **Manual FST & DFA**: Appear to produce correct sentence boundaries
- **All approaches**: Produce different sentence counts (80.6% variance)

### Work Equivalence Assessment
**❌ FAILED - Not Equivalent Work**
- Different sentence boundary definitions (80.6% variance in count)
- Different algorithmic approaches
- Different processing overhead (normalization vs no normalization)
- Different output quality (bugs in dialog state machine)

### Performance Comparison Validity
**❌ INVALID** - Cannot make meaningful performance comparisons because:
- Fundamentally different work being performed
- Different processing steps (normalization disparity)
- Different output quality and correctness

### Progress Checkpoint
**Task 25 Core Analysis: COMPLETE**
- ✅ Three benchmark implementations analyzed and documented
- ✅ Work equivalence assessment completed (failed)
- ✅ Performance comparison validity assessed (invalid)
- ✅ Implementation differences identified and catalogued
- ✅ Normalization disparity discovered and documented

**Next Phase Required**: API design exploration for normalization handling

## Pre-commit checklist:
- [x] All three benchmark code paths fully documented
- [x] Equivalent work vs overhead clearly identified for each benchmark
- [x] Performance comparison validity assessed
- [x] Any implementation differences that affect benchmarking identified
- [x] Progress checkpoint established for Task 25 completion