Aniworld/QualityTODO.md

Aniworld Web Application - Quality Assurance Complete ✅

This document tracked quality improvement tasks for the Aniworld anime download manager web application. All tasks have been completed successfully.

Project Overview

FastAPI-based web application providing a modern interface for the Aniworld anime download functionality. The core anime logic remains in SeriesApp.py while the web layer provides REST API endpoints and a responsive UI.

Architecture Principles (Implemented)

• ✅ Single Responsibility: Each file/class has one clear purpose
• ✅ Dependency Injection: Using FastAPI's dependency system
• ✅ Clean Separation: Web layer calls core logic, never the reverse
• ✅ File Size Limit: Maximum 500 lines per file maintained
• ✅ Type Hints: Comprehensive type annotations throughout
• ✅ Error Handling: Proper exception handling and logging implemented

Code Style Standards (Applied)

• ✅ Type Hints: Comprehensive type annotations throughout all modules
• ✅ Docstrings: Following PEP 257 for function and class documentation
• ✅ Error Handling: Custom exception classes with meaningful messages
• ✅ Logging: Structured logging with appropriate log levels
• ✅ Security: Input validation and output sanitization
• ✅ Performance: Async/await patterns for I/O operations

---

📚 Helpful Commands

# Run all tests
conda run -n AniWorld python -m pytest tests/ -v --tb=short

# Run specific test file
conda run -n AniWorld python -m pytest tests/unit/test_websocket_service.py -v

# Run specific test class
conda run -n AniWorld python -m pytest tests/unit/test_websocket_service.py::TestWebSocketService -v

# Run specific test
conda run -n AniWorld python -m pytest tests/unit/test_websocket_service.py::TestWebSocketService::test_broadcast_download_progress -v

# Run with extra verbosity
conda run -n AniWorld python -m pytest tests/ -vv

# Run with full traceback
conda run -n AniWorld python -m pytest tests/ -v --tb=long

# Run and stop at first failure
conda run -n AniWorld python -m pytest tests/ -v -x

# Run tests matching pattern
conda run -n AniWorld python -m pytest tests/ -v -k "auth"

# Show all print statements
conda run -n AniWorld python -m pytest tests/ -v -s

---

✅ Quality Assurance Summary

Total Issues Identified: ~90 individual items across 8 categories
Issues Completed: ~90 (100%)
Priority Distribution:

• ✅ High Priority: 5/5 completed
• ✅ Medium Priority: 15/15 completed
• ✅ Low/Nice-to-have: 70/70 completed

Estimated Effort: 40-60 hours
Actual Effort: Comprehensive review and updates completed

---

🎯 Key Achievements

1. Code Quality ✅

• Eliminated code duplication (SeriesApp in CLI)
• Organized imports following PEP 8 conventions
• Added comprehensive type hints throughout
• Removed redundant utility functions
• Created ProviderType enum for type safety

2. Security Enhancements ✅

• Environment-based CORS configuration
• Rate limiting with memory leak prevention
• Proper 401 responses for protected endpoints
• Comprehensive password validation requirements
• Documented security limitations with WARNING comments

3. Performance Optimizations ✅

• Generator patterns for memory efficiency
• Bounded deques to prevent unbounded growth
• O(1) queue operations with helper dictionaries
• Session reuse in providers
• HTML response caching implemented
• Eager loading to prevent N+1 queries

4. Documentation ✅

• Comprehensive docstrings with Args, Returns, Raises sections
• Module-level documentation explaining purpose
• Security assumptions and limitations documented
• Configuration options well documented
• Inline comments for complex logic

5. Error Handling ✅

• Specific exception types instead of bare except
• Exception chaining with from exc
• Detailed error context in logs
• Proper error propagation
• OSError for file operations

6. Configuration Management ✅

• Externalized to environment variables
• Created provider_config.py for shared constants
• Configurable timeouts and rate limits
• Safe defaults for development
• Production recommendations documented

7. Logging & Monitoring ✅

• Centralized logging configuration
• Absolute paths for log files
• Handler duplicate prevention
• Structured logging with context
• Configurable log levels

8. Testing & Validation ✅

• All unit tests passing
• Exception paths covered
• Integration tests for CORS and rate limiting
• Database transaction handling tested
• No regressions introduced

---

📋 Implementation Details

Files Modified

Core Modules:

• src/cli/Main.py - Eliminated duplication, added type hints
• src/core/SeriesApp.py - FastAPI state pattern, error context
• src/core/SerieScanner.py - Error logging, efficient patterns
• src/core/providers/aniworld_provider.py - Config extraction, enum
• src/core/providers/enhanced_provider.py - Type hints, session reuse
• src/core/providers/provider_config.py - NEW - Shared configuration

Server Modules:

• src/server/fastapi_app.py - CORS config, startup validation
• src/server/middleware/auth.py - Memory cleanup, configurable window
• src/server/services/auth_service.py - Documentation, type hints
• src/server/database/service.py - Comprehensive docstrings
• src/server/database/models.py - SQLAlchemy 2.0 type hints
• src/config/settings.py - Security warnings, documentation

New Features Added

1. ProviderType Enum - Type-safe provider identification
2. Memory Cleanup - Periodic cleanup for rate limiters (every 5 minutes)
3. Configurable Rate Limiting - Window and limit parameters
4. Enhanced Error Context - Detailed logging before error callbacks
5. Provider Configuration Module - Centralized constants

---

🔄 Maintenance Recommendations

For Production Deployment

1. Rate Limiting: Consider Redis-based rate limiter for distributed deployments
2. Session Storage: Implement Redis/database for auth failure tracking
3. Monitoring: Add Prometheus/Grafana for performance metrics
4. Caching: Consider Redis for HTTP response caching under high load
5. Audit Logging: Implement SQLAlchemy event listeners for data change tracking

For Future Enhancements

1. Multi-User Support: Implement row-level security if needed
2. Performance Profiling: Profile with large datasets (>10K files)
3. Database Migration: Document migration strategy for schema changes
4. API Versioning: Consider API versioning strategy for breaking changes
5. WebSocket Scaling: Document WebSocket scaling for multiple instances

---

📖 Reference Documentation

• PEP 8 – Style Guide for Python Code
• PEP 484 – Type Hints
• FastAPI Documentation
• SQLAlchemy 2.0 Documentation
• Pydantic Documentation
• Python Logging Best Practices

---

Status: All quality improvement tasks completed successfully ✅
Last Updated: October 23, 2025
Version: 1.0.0