Implement comprehensive unit test suite for BlockPy backend with sample data generation

[Copilot]

This PR implements a comprehensive unit test suite for the BlockPy education platform backend, addressing the need for thorough testing infrastructure and sample data generation capabilities.

What's Included

🏗️ Test Infrastructure

• Base test classes with automatic database setup/teardown using in-memory SQLite
• Factory pattern for creating realistic test data across all models
• Test runner with selective execution for different test categories
• Proper test isolation ensuring each test runs independently

📊 Comprehensive Model Coverage (64+ tests)

• User Model (9 tests): Authentication, password hashing, roles, relationships
• Course Model (12 tests): Creation, visibility settings, ownership, relationships
• Assignment Model (12 tests): Different types, status workflows, submissions
• AssignmentGroup Model (13 tests): Categories, positioning, forking functionality
• Submission Model (18+ tests): Status transitions, grading workflows, code storage

🌐 API Endpoint Testing (20 tests)

• Basic endpoints: Root access, static files, health checks
• BlockPy endpoints: Assignment loading, submission handling, parameter validation
• Error handling: Graceful handling of invalid parameters and missing data

🔄 End-to-End Workflow Testing (6 scenarios)

Complete user journeys including:

• Instructor course creation and management
• Student enrollment and assignment submission
• Assignment grading and feedback workflows
• Multi-student collaboration scenarios

🏭 Sample Data Generation

Robust factories for creating test data:

from tests.factories import SampleDataGenerator

# Creates complete course with instructor, students, assignments, submissions
sample_data = SampleDataGenerator.create_sample_course_with_data()
course = sample_data['course']
students = sample_data['students'] 
assignments = sample_data['assignments']

Usage

Run all tests:

python run_tests.py

Run specific categories:

python run_tests.py --models      # Model tests only
python run_tests.py --controllers # API endpoint tests
python run_tests.py --workflows   # Integration tests

Individual test execution:

python -m unittest tests.models.test_user -v

Benefits

• Regression Detection: Catches breaking changes early in development
• Documentation: Tests serve as living documentation of expected behavior
• Refactoring Safety: Enables confident code changes and refactoring
• Development Support: Realistic sample data for development and debugging
• Quality Assurance: Ensures API endpoints handle edge cases properly

The test suite provides a solid foundation for maintaining code quality and enables confident development of the BlockPy platform going forward.

Fixes #71.

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