markitect-main/tests/test_datamodel_optimizer.py.skip

"""
Tests for the Datamodel Optimizer Agent

Validates that the datamodel optimization tool correctly identifies
optimization opportunities and provides accurate assessments.
"""

import pytest
import tempfile
from pathlib import Path
from tools.datamodel_optimizer import (
    DatamodelDiscovery,
    UsageAnalyzer,
    OptimizationAnalyzer,
    OptimizationReporter
)


class TestDatamodelOptimizer:
    """Test the datamodel optimizer functionality."""

    @pytest.fixture
    def temp_project(self):
        """Create a temporary project with sample datamodels."""
        with tempfile.TemporaryDirectory() as tmpdir:
            project_path = Path(tmpdir)

            # Create sample datamodel with optimization opportunities
            sample_model = """
from dataclasses import dataclass
from datetime import datetime
from enum import Enum

class Status(Enum):
    ACTIVE = "active"
    INACTIVE = "inactive"

@dataclass
class SampleModel:
    id: int
    name: str
    status: Status
    created_at: datetime
    description: str = ""
"""

            # Create sample usage with verbose patterns
            sample_usage = """
from models import SampleModel, Status

def format_models(models):
    # Verbose serialization pattern
    data = []
    for model in models:
        item = {
            'id': model.id,
            'name': model.name,
            'status': model.status.value,
            'created_at': model.created_at.strftime('%Y-%m-%d'),
            'description': model.description[:50] + '...' if len(model.description) > 50 else model.description
        }
        data.append(item)
    return data

def display_model(model):
    # Repetitive formatting
    status_display = model.status.value.title()
    formatted_date = model.created_at.strftime('%Y-%m-%d') if model.created_at else 'N/A'
    short_desc = model.description[:40] + '...' if len(model.description) > 40 else model.description
    return f"{model.name} ({status_display}) - {formatted_date} - {short_desc}"
"""

            # Create sample test with dict mocks
            sample_test = """
from unittest.mock import Mock
import pytest

def test_model_processing():
    # Dictionary mock instead of real object
    mock_model = {
        'id': 1,
        'name': 'Test',
        'status': 'active',  # String instead of enum!
        'created_at': '2023-01-01',
        'description': 'Test description'
    }

    result = process_model(mock_model)
    assert result is not None
"""

            # Write files
            (project_path / "models.py").write_text(sample_model)
            (project_path / "usage.py").write_text(sample_usage)
            (project_path / "test_models.py").write_text(sample_test)

            yield project_path

    def test_datamodel_discovery(self, temp_project):
        """Test that datamodel discovery works correctly."""
        discovery = DatamodelDiscovery(temp_project)
        datamodels = discovery.discover_datamodels()

        assert "SampleModel" in datamodels
        model = datamodels["SampleModel"]

        assert model.name == "SampleModel"
        assert model.is_dataclass is True
        assert model.is_pydantic is False
        assert len(model.fields) == 5
        assert "id" in model.fields
        assert "name" in model.fields
        assert "status" in model.fields

    def test_usage_pattern_analysis(self, temp_project):
        """Test that usage pattern analysis identifies optimization opportunities."""
        discovery = DatamodelDiscovery(temp_project)
        datamodels = discovery.discover_datamodels()

        analyzer = UsageAnalyzer(temp_project, datamodels)
        patterns = analyzer.analyze_usage_patterns()

        # Should find formatting patterns
        formatting_patterns = [p for p in patterns if p.pattern_type in
                             ['date_formatting', 'enum_formatting', 'truncation']]
        assert len(formatting_patterns) > 0

        # Should find serialization patterns
        serialization_patterns = [p for p in patterns if p.pattern_type in
                                ['verbose_serialization', 'dict_building']]
        assert len(serialization_patterns) > 0

        # Should find test patterns
        test_patterns = [p for p in patterns if p.pattern_type == 'dict_test_data']
        assert len(test_patterns) > 0

    def test_optimization_opportunities(self, temp_project):
        """Test that optimization opportunities are correctly identified."""
        discovery = DatamodelDiscovery(temp_project)
        datamodels = discovery.discover_datamodels()

        analyzer = UsageAnalyzer(temp_project, datamodels)
        patterns = analyzer.analyze_usage_patterns()

        optimizer = OptimizationAnalyzer(datamodels, patterns)
        opportunities = optimizer.analyze_opportunities()

        # Should identify property opportunities
        property_ops = [op for op in opportunities if op.opportunity_type == 'property']
        assert len(property_ops) > 0

        # Should identify serialization opportunities
        serialization_ops = [op for op in opportunities if op.opportunity_type == 'serialization']
        assert len(serialization_ops) > 0

        # Should identify test alignment opportunities
        test_ops = [op for op in opportunities if op.opportunity_type == 'test_alignment']
        assert len(test_ops) > 0

    def test_optimization_reporter(self, temp_project):
        """Test that optimization reports are generated correctly."""
        discovery = DatamodelDiscovery(temp_project)
        datamodels = discovery.discover_datamodels()

        analyzer = UsageAnalyzer(temp_project, datamodels)
        patterns = analyzer.analyze_usage_patterns()

        optimizer = OptimizationAnalyzer(datamodels, patterns)
        opportunities = optimizer.analyze_opportunities()

        reporter = OptimizationReporter(datamodels, patterns, opportunities)

        # Test summary report
        summary = reporter.generate_summary_report()
        assert "Total Datamodels Found" in summary
        assert "Optimization Opportunities" in summary
        assert "SampleModel" in summary

        # Test detailed report
        detailed = reporter.generate_detailed_report("SampleModel")
        assert "Detailed Analysis: SampleModel" in detailed
        assert "Model Information" in detailed
        assert "Optimization Opportunities" in detailed

        # Test JSON report
        json_report = reporter.generate_json_report()
        assert '"total_datamodels"' in json_report
        assert '"total_opportunities"' in json_report


    def test_impact_scoring(self, temp_project):
        """Test that impact scoring works correctly."""
        discovery = DatamodelDiscovery(temp_project)
        datamodels = discovery.discover_datamodels()

        analyzer = UsageAnalyzer(temp_project, datamodels)
        patterns = analyzer.analyze_usage_patterns()

        optimizer = OptimizationAnalyzer(datamodels, patterns)
        opportunities = optimizer.analyze_opportunities()

        # All opportunities should have reasonable impact scores
        for opportunity in opportunities:
            assert 1 <= opportunity.impact_score <= 10
            assert opportunity.loc_reduction_estimate >= 0

        # High complexity patterns should have higher impact scores
        high_impact = [op for op in opportunities if op.impact_score >= 7]
        assert len(high_impact) > 0


class TestDatamodelOptimizerCLI:
    """Test the CLI interface of the datamodel optimizer."""

    def test_cli_help(self):
        """Test that CLI help works."""
        import subprocess
        result = subprocess.run(['python', 'tools/datamodel_optimizer.py', '--help'],
                              capture_output=True, text=True)
        assert result.returncode == 0
        assert 'Datamodel Optimization Analysis Tool' in result.stdout

    def test_cli_summary_format(self):
        """Test that CLI summary format works."""
        import subprocess
        result = subprocess.run(['python', 'tools/datamodel_optimizer.py', '--format', 'summary'],
                              capture_output=True, text=True, cwd=Path(__file__).parent.parent)
        assert result.returncode == 0
        assert 'Total Datamodels Found' in result.stdout
        assert 'Optimization Opportunities' in result.stdout


if __name__ == '__main__':
    pytest.main([__file__])