"""
Testing Agent - Test generation, coverage analysis, and test improvement.

This agent performs comprehensive test automation by:
1. Analyzing code for test coverage gaps
2. Generating test cases for uncovered paths
3. Improving existing test quality and coverage
4. Identifying integration test opportunities
"""

from typing import Any

from .base_agent import BaseCodeAnalysisAgent


class TestingAgent(BaseCodeAnalysisAgent):
    """
    AI agent specialized in test generation and improvement.

    Uses Code Scalpel MCP tools to:
    - Analyze code coverage and identify gaps
    - Generate unit tests for functions/methods
    - Create integration test scenarios
    - Improve existing test quality and maintainability
    """

    def __init__(self, workspace_root: str | None = None):
        super().__init__(workspace_root)
        self.testing_thresholds = {
            "min_coverage": 80,
            "min_branch_coverage": 75,
            "max_uncovered_functions": 5,
            "min_test_per_function": 1,
        }

    async def observe(self, target: str) -> dict[str, Any]:
        """
        Observe the target file and analyze coverage.

        Args:
            target: Path to the file to analyze for testing

        Returns:
            Dict containing file analysis and coverage information
        """
        self.logger.info(f"Observing file for test coverage: {target}")

        # Get file context
        file_info = await self.observe_file(target)
        if not file_info.get("success"):
            return file_info

        # Analyze symbol usage to understand test relationships
        symbol_analysis = {}
        for func_name in file_info.get("functions", []):
            refs = await self.find_symbol_usage(func_name, self.context.workspace_root)
            if refs.get("success"):
                symbol_analysis[func_name] = refs

        return {
            "success": True,
            "file_info": file_info,
            "symbol_analysis": symbol_analysis,
            "target": target,
        }

    async def orient(self, observations: dict[str, Any]) -> dict[str, Any]:
        """
        Analyze observations to identify testing opportunities.

        Args:
            observations: Results from observe phase

        Returns:
            Dict containing coverage analysis and test generation opportunities
        """
        self.logger.info("Analyzing coverage and test opportunities")

        file_info = observations.get("file_info", {})
        testing_ops = []

        # Analyze coverage gaps
        coverage_analysis = self._analyze_coverage(file_info)
        testing_ops.extend(coverage_analysis)

        # Identify edge cases and error paths
        edge_cases = self._identify_edge_cases(file_info)
        testing_ops.extend(edge_cases)

        # Detect integration test opportunities
        integration_ops = self._identify_integration_tests(observations.get("symbol_analysis", {}))
        testing_ops.extend(integration_ops)

        # Prioritize by coverage impact
        prioritized = self._prioritize_tests(testing_ops)

        return {
            "success": True,
            "test_opportunities": prioritized,
            "analysis_details": {
                "coverage_gaps": coverage_analysis,
                "edge_cases": edge_cases,
                "integration_tests": integration_ops,
            },
        }

    async def decide(self, analysis: dict[str, Any]) -> dict[str, Any]:
        """
        Decide which tests to generate.

        Args:
            analysis: Results from orient phase

        Returns:
            Dict containing planned test generation actions
        """
        self.logger.info("Planning test generation")

        opportunities = analysis.get("test_opportunities", [])

        # Filter for high-impact tests
        high_impact_tests = [
            op for op in opportunities if op.get("coverage_impact", 0) > 5 and op.get("complexity", 0) < 10
        ]

        # Create action plan
        actions = []
        for test_op in high_impact_tests[:10]:  # Generate up to 10 tests
            action = {
                "type": test_op.get("type"),
                "description": test_op.get("description"),
                "target_function": test_op.get("target"),
                "expected_coverage_gain": test_op.get("coverage_impact"),
                "edge_cases": test_op.get("edge_cases", []),
            }
            actions.append(action)

        return {
            "success": True,
            "planned_tests": actions,
            "total_opportunities": len(opportunities),
            "scheduled_tests": len(actions),
        }

    async def act(self, decisions: dict[str, Any]) -> dict[str, Any]:
        """
        Execute test generation and improvements.

        Args:
            decisions: Results from decide phase

        Returns:
            Dict containing test generation results
        """
        self.logger.info("Generating tests")

        planned = decisions.get("planned_tests", [])
        results = []

        for test_plan in planned:
            result = await self._generate_test(test_plan)
            results.append(result)

        return {
            "success": all(r.get("success", False) for r in results),
            "tests_generated": len([r for r in results if r.get("success")]),
            "total_planned": len(planned),
            "coverage_gain": sum(r.get("coverage_gain", 0) for r in results),
            "results": results,
        }

    def _analyze_coverage(self, file_info: dict[str, Any]) -> list[dict[str, Any]]:
        """Analyze code coverage and identify gaps."""
        gaps = []
        # Implementation placeholder
        return gaps

    def _identify_edge_cases(self, file_info: dict[str, Any]) -> list[dict[str, Any]]:
        """Identify edge cases and error paths."""
        cases = []
        # Implementation placeholder
        return cases

    def _identify_integration_tests(self, symbol_analysis: dict[str, Any]) -> list[dict[str, Any]]:
        """Identify opportunities for integration testing."""
        tests = []
        # Implementation placeholder
        return tests

    def _prioritize_tests(self, tests: list[dict[str, Any]]) -> list[dict[str, Any]]:
        """Prioritize tests by coverage impact and complexity."""
        return sorted(
            tests,
            key=lambda x: x.get("coverage_impact", 0) / max(x.get("complexity", 1), 1),
            reverse=True,
        )

    async def _generate_test(self, test_plan: dict[str, Any]) -> dict[str, Any]:
        """Generate a test case."""
        return {
            "success": True,
            "target_function": test_plan.get("target_function"),
            "test_count": 1,
            "coverage_gain": 5,
        }
