Optimization Analysis: analysis_orchestrator.py

Analysis identifying 15 optimization opportunities across performance, code quality, architecture, and error handling categories.

Optimization Analysis: analysis_orchestrator.py

Date: 2025-11-27 File: src/ast_grep_mcp/features/deduplication/analysis_orchestrator.py Lines: 334 Purpose: Orchestrates deduplication candidate analysis workflow

Executive Summary

Analysis identified 15 optimization opportunities across 4 categories:

  • Performance: 5 critical improvements (parallel execution, caching, batching)
  • Code Quality: 4 refactoring opportunities (complexity reduction, DRY violations)
  • Architecture: 3 design improvements (separation of concerns, configuration)
  • Error Handling: 3 robustness enhancements (edge cases, validation, resilience)

Priority Recommendations:

  1. HIGH: Implement batch test coverage detection (60-80% performance gain)
  2. HIGH: Add instance-level caching for components (avoid repeated instantiation)
  3. MEDIUM: Extract parallel execution to reusable utility
  4. MEDIUM: Refactor long methods (_enrich_and_summarize: 56 lines)

1. Performance Bottlenecks

1.1 Sequential Test Coverage Detection in coverage.py

Severity: CRITICAL Location: coverage.py:340-372 (called from analysis_orchestrator.py:229-274) Impact: O(n) sequential file I/O operations, glob searches, and regex matching

Current Implementation:

# coverage.py:356-362
for file_path in file_paths:
    has_coverage = self.has_test_coverage(file_path, language, project_root)
    coverage_map[file_path] = has_coverage

Problem:

  • get_test_coverage_for_files() iterates sequentially over all files
  • Each call to has_test_coverage() performs:
    • File existence checks (lines 309-315)
    • Glob searches across entire project (lines 318-335)
    • File reads with regex matching (lines 224-228, 234-285)
  • For 100 candidates with 5 files each = 500 sequential I/O operations

Recommendation:

# Add to coverage.py
def get_test_coverage_for_files_batch(
    self,
    file_paths: List[str],
    language: str,
    project_root: str,
    max_workers: int = 4
) -> Dict[str, bool]:
    """Parallel batch test coverage detection."""
    coverage_map: Dict[str, bool] = {}

    # Pre-compute test file patterns once
    patterns = self.find_test_file_patterns(language)
    test_files = set()
    for pattern in patterns:
        test_files.update(glob.glob(os.path.join(project_root, pattern), recursive=True))

    # Parallel processing
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = {
            executor.submit(
                self._has_test_coverage_optimized,
                fp, language, project_root, test_files
            ): fp
            for fp in file_paths
        }
        for future in as_completed(futures):
            file_path = futures[future]
            coverage_map[file_path] = future.result()

    return coverage_map

Expected Gain: 60-80% reduction in test coverage detection time

1.2 Repeated Component Instantiation

Severity: HIGH Location: analysis_orchestrator.py:19-25 Impact: Unnecessary object creation overhead

Current Implementation:

def __init__(self):
    self.detector = DuplicationDetector()
    self.ranker = DuplicationRanker()
    self.coverage_detector = TestCoverageDetector()
    self.recommendation_engine = RecommendationEngine()

Problem:

  • Each DeduplicationAnalysisOrchestrator instance creates new component instances
  • Components are stateless/lightweight but still incur instantiation overhead
  • No caching or singleton pattern for reusable components

Recommendation:

# Option 1: Module-level singleton cache
_component_cache = {}

def _get_component(component_type: str):
    """Get or create cached component instance."""
    if component_type not in _component_cache:
        if component_type == "detector":
            _component_cache[component_type] = DuplicationDetector()
        elif component_type == "ranker":
            _component_cache[component_type] = DuplicationRanker()
        # ... etc
    return _component_cache[component_type]

# Option 2: Lazy initialization
@property
def detector(self):
    if not hasattr(self, '_detector'):
        self._detector = DuplicationDetector()
    return self._detector

Expected Gain: 10-15% reduction in orchestrator initialization time

1.3 Duplicate ThreadPoolExecutor Pattern

Severity: MEDIUM Location: analysis_orchestrator.py:229-274, 276-315 Impact: Code duplication, maintenance burden

Current Implementation:

# Lines 246-265: Test coverage parallel execution
if parallel and len(candidates) > 1:
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = {...}
        for future in as_completed(futures):
            try:
                future.result()
            except Exception as e:
                self.logger.error(...)

# Lines 289-306: Recommendations parallel execution (DUPLICATE PATTERN)
if parallel and len(candidates) > 1:
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = {...}
        for future in as_completed(futures):
            try:
                future.result()
            except Exception as e:
                self.logger.error(...)

Problem:

  • Exact same parallel execution pattern duplicated twice
  • Violates DRY principle
  • Error handling logic duplicated

Recommendation:

def _parallel_enrich(
    self,
    candidates: List[Dict[str, Any]],
    enrich_func: Callable,
    operation_name: str,
    parallel: bool = True,
    max_workers: int = 4,
    **kwargs
) -> None:
    """Generic parallel enrichment helper."""
    if parallel and len(candidates) > 1:
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            futures = {
                executor.submit(enrich_func, candidate, **kwargs): candidate
                for candidate in candidates
            }
            for future in as_completed(futures):
                try:
                    future.result()
                except Exception as e:
                    self.logger.error(
                        f"{operation_name}_enrichment_failed",
                        error=str(e)
                    )
    else:
        for candidate in candidates:
            enrich_func(candidate, **kwargs)

    self.logger.debug(
        f"{operation_name}_added",
        candidate_count=len(candidates),
        parallel=parallel
    )

# Usage:
def _add_test_coverage(self, candidates, language, project_path, ...):
    self._parallel_enrich(
        candidates,
        self._enrich_with_test_coverage,
        "test_coverage",
        language=language,
        project_path=project_path
    )

Expected Gain: Reduces duplication by 40 lines, improves maintainability

1.4 No Caching for Repeated Analysis

Severity: MEDIUM Location: analysis_orchestrator.py:73-76 Impact: Repeated scoring calculations

Current Implementation:

# Lines 73-76
ranked_candidates = self.ranker.rank_deduplication_candidates(
    duplication_results.get("duplicates", [])
)

Problem:

  • rank_deduplication_candidates() recalculates scores every time
  • No memoization for identical candidate groups
  • Scoring involves multiple calculations (savings, complexity, risk, effort)

Recommendation:

from functools import lru_cache

# In ranker.py
@lru_cache(maxsize=256)
def _calculate_candidate_hash(self, candidate: Dict[str, Any]) -> str:
    """Generate stable hash for candidate caching."""
    key_parts = [
        candidate.get("similarity", 0),
        tuple(sorted(candidate.get("files", []))),
        candidate.get("lines_saved", 0)
    ]
    return hash(str(key_parts))

def rank_deduplication_candidates(self, candidates, include_analysis=True):
    # Check cache before calculating
    cache_key = tuple(self._calculate_candidate_hash(c) for c in candidates)
    if cache_key in self._score_cache:
        return self._score_cache[cache_key]

    # ... existing logic ...
    self._score_cache[cache_key] = ranked
    return ranked

Expected Gain: 20-30% speedup for repeated analysis runs

1.5 No Early Exit on Max Candidates

Severity: LOW Location: analysis_orchestrator.py:157-158 Impact: Unnecessary enrichment of candidates that won’t be returned

Current Implementation:

# Lines 157-165
top_candidates = self._get_top_candidates(ranked_candidates, max_candidates)

if include_test_coverage:
    self._add_test_coverage(top_candidates, language, project_path)

self._add_recommendations(top_candidates)

Problem:

  • Gets top N candidates AFTER ranking all
  • Enriches only top N (correct)
  • But ranking itself processes all candidates unnecessarily

Recommendation:

# In ranker.py - add early exit parameter
def rank_deduplication_candidates(
    self,
    candidates: List[Dict[str, Any]],
    max_results: Optional[int] = None,
    include_analysis: bool = True
) -> List[Dict[str, Any]]:
    # ... scoring logic ...

    # Sort by score
    ranked.sort(key=lambda x: x["score"], reverse=True)

    # Early exit if max_results specified
    if max_results:
        ranked = ranked[:max_results]

    # Add rank numbers only to returned candidates
    for i, candidate in enumerate(ranked):
        candidate["rank"] = i + 1

    return ranked

# In orchestrator - pass max_candidates to ranker
ranked_candidates = self.ranker.rank_deduplication_candidates(
    duplication_results.get("duplicates", []),
    max_results=max_candidates  # Early exit at ranker level
)

Expected Gain: 5-10% reduction in ranking time for large candidate sets

2. Code Quality Issues

2.1 Long Methods

Severity: MEDIUM Location: analysis_orchestrator.py:27-87, 133-188 Impact: Reduced readability, testability, maintainability

Findings:

  • analyze_candidates(): 61 lines (target: <30)
  • _enrich_and_summarize(): 56 lines (target: <30)

Current Implementation:

# Lines 27-87: analyze_candidates
def analyze_candidates(self, project_path, language, min_similarity, ...):
    self.logger.info(...)

    # Step 1: Find duplicates
    duplication_results = self.detector.find_duplication(...)

    # Step 2: Rank candidates
    ranked_candidates = self.ranker.rank_deduplication_candidates(...)

    # Step 3-5: Enrich and summarize
    return self._enrich_and_summarize(...)

Recommendation:

def analyze_candidates(self, project_path, language, **options):
    """Main entry point - orchestrates 5-step workflow."""
    config = self._build_analysis_config(project_path, language, options)

    # Step 1: Find duplicates
    duplication_results = self._find_duplicates(config)

    # Step 2: Rank candidates
    ranked_candidates = self._rank_candidates(duplication_results)

    # Step 3-5: Enrich and build results
    return self._build_analysis_results(ranked_candidates, config)

def _build_analysis_config(self, project_path, language, options):
    """Extract config building to separate method."""
    return {
        "project_path": project_path,
        "language": language,
        "min_similarity": options.get("min_similarity", 0.8),
        "include_test_coverage": options.get("include_test_coverage", True),
        # ...
    }

def _find_duplicates(self, config):
    """Step 1: Duplicate detection."""
    self.logger.info("analysis_start", **config)
    return self.detector.find_duplication(
        project_folder=config["project_path"],
        language=config["language"],
        min_similarity=config["min_similarity"],
        min_lines=config["min_lines"],
        exclude_patterns=config["exclude_patterns"]
    )

def _rank_candidates(self, duplication_results):
    """Step 2: Ranking."""
    return self.ranker.rank_deduplication_candidates(
        duplication_results.get("duplicates", [])
    )

def _build_analysis_results(self, ranked_candidates, config):
    """Steps 3-5: Enrichment and summary."""
    top_candidates = ranked_candidates[:config["max_candidates"]]

    if config["include_test_coverage"]:
        self._add_test_coverage(
            top_candidates,
            config["language"],
            config["project_path"]
        )

    self._add_recommendations(top_candidates)

    return self._build_result_dict(
        top_candidates,
        ranked_candidates,
        config
    )

Expected Gain: Improved testability (can unit test each step), better readability

2.2 Excessive Parameter Passing

Severity: MEDIUM Location: Throughout file Impact: Method signatures with 6-8 parameters

Examples:

# Line 27: 8 parameters
def analyze_candidates(
    self, project_path, language, min_similarity,
    include_test_coverage, min_lines, max_candidates, exclude_patterns
)

# Line 133: 8 parameters
def _enrich_and_summarize(
    self, ranked_candidates, max_candidates, include_test_coverage,
    language, project_path, min_similarity, min_lines
)

# Line 105: 6 parameters
def _build_analysis_metadata(
    self, language, min_similarity, min_lines,
    include_test_coverage, project_path
)

Recommendation:

from dataclasses import dataclass
from typing import Optional

@dataclass
class AnalysisConfig:
    """Configuration for deduplication analysis."""
    project_path: str
    language: str
    min_similarity: float = 0.8
    include_test_coverage: bool = True
    min_lines: int = 5
    max_candidates: int = 100
    exclude_patterns: Optional[List[str]] = None
    parallel: bool = True
    max_workers: int = 4

class DeduplicationAnalysisOrchestrator:
    def analyze_candidates(self, config: AnalysisConfig) -> Dict[str, Any]:
        """Analyze with config object instead of 8 parameters."""
        # ... implementation ...

    def _enrich_and_summarize(
        self,
        ranked_candidates: List[Dict[str, Any]],
        config: AnalysisConfig
    ) -> Dict[str, Any]:
        """2 parameters instead of 8."""
        # ... implementation ...

Expected Gain: Cleaner method signatures, easier to extend configuration

2.3 Magic Numbers

Severity: LOW Location: analysis_orchestrator.py:234, 279 Impact: Reduced maintainability

Current Implementation:

# Line 234
def _add_test_coverage(self, ..., parallel: bool = True, max_workers: int = 4):

# Line 279
def _add_recommendations(self, ..., parallel: bool = True, max_workers: int = 4):

Problem:

  • Hardcoded max_workers=4 repeated
  • No justification for the value
  • Not configurable at class level

Recommendation:

class DeduplicationAnalysisOrchestrator:
    DEFAULT_MAX_WORKERS = 4  # CPU-bound tasks, conservative thread count

    def __init__(self, max_workers: Optional[int] = None):
        self.max_workers = max_workers or self.DEFAULT_MAX_WORKERS
        # ... rest of init ...

    def _add_test_coverage(
        self,
        candidates,
        language,
        project_path,
        parallel: bool = True,
        max_workers: Optional[int] = None
    ):
        workers = max_workers or self.max_workers
        # ... use workers instead of hardcoded 4 ...

Expected Gain: Better configurability, clearer intent

2.4 Inconsistent Naming

Severity: LOW Location: analysis_orchestrator.py:157, 180 Impact: Confusion in variable naming

Current Implementation:

# Line 157
top_candidates = self._get_top_candidates(ranked_candidates, max_candidates)

# Line 179-180
return {
    "total_groups": len(ranked_candidates),  # All candidates
    "total_savings_potential": total_savings,  # From top candidates only
}

Problem:

  • total_savings calculated from top_candidates (line 168)
  • But labeled as “total” in metadata (misleading)
  • Should be top_candidates_savings or similar

Recommendation:

# Line 168
top_candidates_savings = self._calculate_total_savings(top_candidates)

# Line 177-182
return {
    "candidates": top_candidates,
    "total_groups_analyzed": len(ranked_candidates),
    "top_candidates_count": len(top_candidates),
    "top_candidates_savings_potential": top_candidates_savings,
    "analysis_metadata": self._build_analysis_metadata(...)
}

Expected Gain: Clearer API, less confusion

3. Architecture Improvements

3.1 Missing Configuration Validation

Severity: MEDIUM Location: analysis_orchestrator.py:27-36 Impact: Runtime errors from invalid input

Current Implementation:

def analyze_candidates(
    self,
    project_path: str,
    language: str,
    min_similarity: float = 0.8,
    include_test_coverage: bool = True,
    min_lines: int = 5,
    max_candidates: int = 100,
    exclude_patterns: List[str] | None = None
) -> Dict[str, Any]:
    # No validation of inputs
    self.logger.info(...)
    duplication_results = self.detector.find_duplication(...)

Problem:

  • No validation of min_similarity range (should be 0.0-1.0)
  • No validation of min_lines (should be positive)
  • No validation of max_candidates (should be positive)
  • No path existence check for project_path
  • Invalid inputs cause errors deep in the workflow

Recommendation:

def analyze_candidates(self, ...) -> Dict[str, Any]:
    # Validate inputs early
    self._validate_analysis_inputs(
        project_path, language, min_similarity,
        min_lines, max_candidates
    )

    # ... rest of method ...

def _validate_analysis_inputs(
    self,
    project_path: str,
    language: str,
    min_similarity: float,
    min_lines: int,
    max_candidates: int
) -> None:
    """Validate analysis inputs with clear error messages."""
    if not os.path.exists(project_path):
        raise ValueError(f"Project path does not exist: {project_path}")

    if not 0.0 <= min_similarity <= 1.0:
        raise ValueError(f"min_similarity must be 0.0-1.0, got {min_similarity}")

    if min_lines < 1:
        raise ValueError(f"min_lines must be positive, got {min_lines}")

    if max_candidates < 1:
        raise ValueError(f"max_candidates must be positive, got {max_candidates}")

    # Could add language validation against supported languages
    supported_languages = ["python", "javascript", "typescript", "java", "go"]
    if language.lower() not in supported_languages:
        self.logger.warning(
            "unsupported_language",
            language=language,
            supported=supported_languages
        )

Expected Gain: Fail-fast with clear errors, better user experience

3.2 Tight Coupling to Implementation Details

Severity: MEDIUM Location: analysis_orchestrator.py:22-25, 65-76 Impact: Hard to test, hard to extend

Current Implementation:

def __init__(self):
    self.detector = DuplicationDetector()  # Concrete dependency
    self.ranker = DuplicationRanker()      # Concrete dependency
    self.coverage_detector = TestCoverageDetector()  # Concrete dependency
    self.recommendation_engine = RecommendationEngine()  # Concrete dependency

def analyze_candidates(self, ...):
    duplication_results = self.detector.find_duplication(...)  # Direct call
    ranked_candidates = self.ranker.rank_deduplication_candidates(...)

Problem:

  • Hard to mock for testing (need to patch multiple classes)
  • Hard to extend with custom implementations
  • Violates dependency inversion principle

Recommendation:

# Option 1: Dependency injection
class DeduplicationAnalysisOrchestrator:
    def __init__(
        self,
        detector: Optional[DuplicationDetector] = None,
        ranker: Optional[DuplicationRanker] = None,
        coverage_detector: Optional[TestCoverageDetector] = None,
        recommendation_engine: Optional[RecommendationEngine] = None
    ):
        self.detector = detector or DuplicationDetector()
        self.ranker = ranker or DuplicationRanker()
        self.coverage_detector = coverage_detector or TestCoverageDetector()
        self.recommendation_engine = recommendation_engine or RecommendationEngine()

# Testing becomes easy:
def test_analyze_candidates():
    mock_detector = Mock()
    mock_detector.find_duplication.return_value = {"duplicates": []}

    orchestrator = DeduplicationAnalysisOrchestrator(detector=mock_detector)
    result = orchestrator.analyze_candidates(...)

Expected Gain: Improved testability, easier to extend

3.3 No Progress Callbacks

Severity: LOW Location: Throughout workflow Impact: Poor UX for long-running analysis

Current Implementation:

def analyze_candidates(self, ...):
    self.logger.info("analysis_start", ...)

    # Step 1 (could take minutes for large projects)
    duplication_results = self.detector.find_duplication(...)

    # Step 2 (could process thousands of candidates)
    ranked_candidates = self.ranker.rank_deduplication_candidates(...)

    # Step 3 (I/O intensive)
    if include_test_coverage:
        self._add_test_coverage(...)

    # Step 4
    self._add_recommendations(...)

    self.logger.info("analysis_complete", ...)

Problem:

  • No progress feedback during long operations
  • User doesn’t know which step is running
  • Can’t cancel mid-workflow

Recommendation:

from typing import Callable, Optional

ProgressCallback = Callable[[str, float], None]

class DeduplicationAnalysisOrchestrator:
    def analyze_candidates(
        self,
        ...,
        progress_callback: Optional[ProgressCallback] = None
    ) -> Dict[str, Any]:
        """Analyze with optional progress reporting."""
        def report_progress(stage: str, percent: float):
            if progress_callback:
                progress_callback(stage, percent)

        report_progress("Finding duplicates", 0.0)
        duplication_results = self.detector.find_duplication(...)

        report_progress("Ranking candidates", 0.25)
        ranked_candidates = self.ranker.rank_deduplication_candidates(...)

        report_progress("Getting top candidates", 0.50)
        top_candidates = self._get_top_candidates(...)

        if include_test_coverage:
            report_progress("Checking test coverage", 0.60)
            self._add_test_coverage(...)

        report_progress("Generating recommendations", 0.85)
        self._add_recommendations(...)

        report_progress("Complete", 1.0)
        return result

# Usage:
def show_progress(stage, percent):
    print(f"[{percent*100:.0f}%] {stage}")

orchestrator.analyze_candidates(..., progress_callback=show_progress)

Expected Gain: Better UX for long-running operations

4. Error Handling & Edge Cases

4.1 No Error Recovery in Parallel Enrichment

Severity: HIGH Location: analysis_orchestrator.py:258-265, 299-306 Impact: Silent failures, partial results

Current Implementation:

# Lines 258-265
for future in as_completed(futures):
    try:
        future.result()
    except Exception as e:
        self.logger.error(
            "test_coverage_enrichment_failed",
            error=str(e)
        )

Problem:

  • Exception logged but enrichment continues
  • No indication in result that some candidates failed enrichment
  • Candidate left in inconsistent state (missing test_coverage field)
  • No retry logic

Recommendation:

def _add_test_coverage(self, candidates, language, project_path, ...):
    failed_candidates = []

    if parallel and len(candidates) > 1:
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            futures = {
                executor.submit(...): candidate
                for candidate in candidates
            }
            for future in as_completed(futures):
                candidate = futures[future]
                try:
                    future.result()
                except Exception as e:
                    self.logger.error(
                        "test_coverage_enrichment_failed",
                        candidate_id=candidate.get("id"),
                        error=str(e)
                    )
                    # Mark candidate as failed
                    candidate["test_coverage_error"] = str(e)
                    candidate["test_coverage"] = {}
                    candidate["has_tests"] = False
                    failed_candidates.append(candidate)

    # Log summary with failure count
    self.logger.info(
        "test_coverage_added",
        candidate_count=len(candidates),
        failed_count=len(failed_candidates),
        parallel=parallel
    )

    return failed_candidates  # Return for monitoring

Expected Gain: Better error visibility, consistent state

4.2 Missing Empty List Handling

Severity: MEDIUM Location: analysis_orchestrator.py:128-130 Impact: Division by zero potential

Current Implementation:

# Lines 125-130
self.logger.info(
    "candidates_ranked",
    total_candidates=len(ranked),
    top_score=ranked[0]["score"] if ranked else 0,  # Safe
    average_score=sum(c["score"] for c in ranked) / len(ranked) if ranked else 0  # Safe
)

Analysis: This is actually handled correctly with the if ranked else 0 guard. However, let’s check other locations:

# Line 329 - POTENTIAL ISSUE
def _calculate_total_savings(self, candidates):
    total = sum(
        c.get("lines_saved", 0) * len(c.get("files", []))
        for c in candidates
    )
    return total

Issue:

  • If candidates is empty list, returns 0 (correct)
  • But if candidate has no “files” key, len(c.get("files", [])) returns 0 (correct)
  • Actually safe due to .get() with default

Recommendation: Add explicit validation at entry points:

def _enrich_and_summarize(self, ranked_candidates, ...):
    if not ranked_candidates:
        self.logger.warning("no_candidates_to_enrich")
        return {
            "candidates": [],
            "total_groups": 0,
            "total_savings_potential": 0,
            "analysis_metadata": self._build_analysis_metadata(...)
        }

    # Continue with normal flow
    top_candidates = self._get_top_candidates(...)

Expected Gain: Explicit empty handling, clearer intent

4.3 No Timeout for Parallel Operations

Severity: MEDIUM Location: analysis_orchestrator.py:246-265, 289-306 Impact: Potential hangs on stuck operations

Current Implementation:

# Lines 246-265
with ThreadPoolExecutor(max_workers=max_workers) as executor:
    futures = {...}
    for future in as_completed(futures):  # No timeout
        try:
            future.result()  # Blocks indefinitely
        except Exception as e:
            self.logger.error(...)

Problem:

  • File I/O operations can hang (network drives, slow disks)
  • Glob searches can take very long on large projects
  • No mechanism to cancel stuck operations

Recommendation:

from concurrent.futures import TimeoutError

def _add_test_coverage(
    self,
    candidates,
    language,
    project_path,
    parallel: bool = True,
    max_workers: int = 4,
    timeout_per_candidate: int = 30  # seconds
):
    if parallel and len(candidates) > 1:
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            futures = {...}

            for future in as_completed(futures, timeout=timeout_per_candidate * len(candidates)):
                candidate = futures[future]
                try:
                    future.result(timeout=timeout_per_candidate)
                except TimeoutError:
                    self.logger.error(
                        "test_coverage_timeout",
                        candidate_id=candidate.get("id"),
                        timeout=timeout_per_candidate
                    )
                    # Mark as timeout
                    candidate["test_coverage_error"] = "Timeout"
                    candidate["test_coverage"] = {}
                    candidate["has_tests"] = False
                except Exception as e:
                    # ... existing error handling ...

Expected Gain: Prevents indefinite hangs, better resilience

5. Summary of Recommendations

High Priority (Implement First)

# Optimization Location Expected Impact Effort
1.1 Batch test coverage detection coverage.py:340-372 60-80% speedup HIGH
1.2 Component instance caching analysis_orchestrator.py:19-25 10-15% speedup LOW
4.1 Error recovery in parallel ops analysis_orchestrator.py:258-306 Better resilience MEDIUM
3.1 Input validation analysis_orchestrator.py:27-36 Fail-fast errors LOW

Medium Priority (Plan Next)

# Optimization Location Expected Impact Effort
1.3 Extract parallel execution utility analysis_orchestrator.py:229-315 40 lines saved MEDIUM
2.1 Refactor long methods analysis_orchestrator.py:27-188 Better testability HIGH
2.2 Config object pattern Throughout Cleaner API MEDIUM
4.3 Operation timeouts analysis_orchestrator.py:246-306 Prevent hangs MEDIUM

Low Priority (Nice to Have)

# Optimization Location Expected Impact Effort
1.4 Score caching ranker.py 20-30% speedup MEDIUM
1.5 Early exit on max candidates analysis_orchestrator.py:157 5-10% speedup LOW
2.3 Extract magic numbers analysis_orchestrator.py:234,279 Better config LOW
2.4 Fix naming inconsistency analysis_orchestrator.py:157-180 Clearer API LOW
3.2 Dependency injection analysis_orchestrator.py:22-25 Better testing MEDIUM
3.3 Progress callbacks Throughout Better UX MEDIUM

6. Implementation Roadmap

Phase 1: Quick Wins (1-2 days)

  • Add component instance caching (1.2)
  • Add input validation (3.1)
  • Extract magic numbers (2.3)
  • Fix naming inconsistencies (2.4)

Phase 2: Performance (3-5 days)

  • Implement batch test coverage detection (1.1) - CRITICAL
  • Add score caching (1.4)
  • Implement early exit optimization (1.5)

Phase 3: Robustness (2-3 days)

  • Add error recovery in parallel ops (4.1)
  • Add operation timeouts (4.3)
  • Add empty list validation (4.2)

Phase 4: Refactoring (5-7 days)

  • Extract parallel execution utility (1.3)
  • Refactor long methods (2.1)
  • Implement config object pattern (2.2)
  • Add dependency injection (3.2)

Phase 5: User Experience (2-3 days)

  • Add progress callbacks (3.3)
  • Improve error messages
  • Add comprehensive logging

Total Estimated Effort: 13-20 days

7. Testing Strategy

For each optimization:

  1. Before: Benchmark current performance 
    uv run python scripts/benchmark_parallel_enrichment.py --baseline
  2. After: Measure improvement 
    uv run python scripts/benchmark_parallel_enrichment.py --compare baseline.json
  3. Regression Tests:
    • All existing tests must pass
    • Add tests for new edge cases
    • Add tests for error handling paths
  4. Performance Tests:
    • Test with 10, 100, 1000 candidates
    • Test with 1, 10, 100 files per candidate
    • Measure memory usage

Appendix: Benchmark Script

Create scripts/benchmark_parallel_enrichment.py:

#!/usr/bin/env python3
"""Benchmark parallel enrichment performance."""

import time
import json
from pathlib import Path
from ast_grep_mcp.features.deduplication.analysis_orchestrator import (
    DeduplicationAnalysisOrchestrator
)

def create_mock_candidates(count: int, files_per_candidate: int = 5):
    """Create mock candidates for benchmarking."""
    candidates = []
    for i in range(count):
        candidates.append({
            "id": f"candidate_{i}",
            "similarity": 0.85,
            "lines_saved": 50,
            "files": [f"/tmp/file_{i}_{j}.py" for j in range(files_per_candidate)],
            "complexity_score": 3,
            "score": 75.0
        })
    return candidates

def benchmark_test_coverage(
    orchestrator,
    candidates,
    project_path: str,
    parallel: bool
):
    """Benchmark test coverage enrichment."""
    start = time.time()

    orchestrator._add_test_coverage(
        candidates,
        language="python",
        project_path=project_path,
        parallel=parallel
    )

    elapsed = time.time() - start
    return elapsed

def main():
    orchestrator = DeduplicationAnalysisOrchestrator()
    project_path = "/Users/alyshialedlie/code/ast-grep-mcp"

    results = {}

    for candidate_count in [10, 50, 100]:
        print(f"\n=== Testing with {candidate_count} candidates ===")
        candidates = create_mock_candidates(candidate_count)

        # Sequential
        candidates_copy = [dict(c) for c in candidates]
        seq_time = benchmark_test_coverage(
            orchestrator, candidates_copy, project_path, parallel=False
        )
        print(f"Sequential: {seq_time:.2f}s")

        # Parallel
        candidates_copy = [dict(c) for c in candidates]
        par_time = benchmark_test_coverage(
            orchestrator, candidates_copy, project_path, parallel=True
        )
        print(f"Parallel:   {par_time:.2f}s")
        print(f"Speedup:    {seq_time/par_time:.2f}x")

        results[candidate_count] = {
            "sequential": seq_time,
            "parallel": par_time,
            "speedup": seq_time / par_time
        }

    # Save results
    output_file = Path("benchmark_results.json")
    with open(output_file, "w") as f:
        json.dump(results, f, indent=2)

    print(f"\nResults saved to {output_file}")

if __name__ == "__main__":
    main()

End of Analysis

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