claude-flow/.claude/worktrees/adr-120-impl/v3/plugins/test-intelligence/src/mcp-tools.ts

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/**
 * Test Intelligence MCP Tools
 *
 * 5 MCP tools for AI-powered test intelligence:
 * 1. test/select-predictive - Predictive test selection using RL
 * 2. test/flaky-detect - Flaky test detection and analysis
 * 3. test/coverage-gaps - Test coverage gap identification
 * 4. test/mutation-optimize - Mutation testing optimization
 * 5. test/generate-suggest - Test case generation suggestions
 */

import type {
  MCPTool,
  MCPToolResult,
  ToolContext,
  SelectPredictiveOutput,
  SelectedTest,
  FlakyDetectOutput,
  FlakyTest,
  CoverageGapsOutput,
  CoverageGap,
  MutationOptimizeOutput,
  OptimizedMutation,
  GenerateSuggestOutput,
  TestSuggestion,
  CodeChange,
} from './types.js';

import {
  SelectPredictiveInputSchema,
  FlakyDetectInputSchema,
  CoverageGapsInputSchema,
  MutationOptimizeInputSchema,
  GenerateSuggestInputSchema,
  successResult,
  errorResult,
} from './types.js';

// ============================================================================
// Default Logger
// ============================================================================

const defaultLogger = {
  debug: (msg: string, meta?: Record<string, unknown>) => console.debug(`[test-intelligence] ${msg}`, meta),
  info: (msg: string, meta?: Record<string, unknown>) => console.info(`[test-intelligence] ${msg}`, meta),
  warn: (msg: string, meta?: Record<string, unknown>) => console.warn(`[test-intelligence] ${msg}`, meta),
  error: (msg: string, meta?: Record<string, unknown>) => console.error(`[test-intelligence] ${msg}`, meta),
};

// ============================================================================
// Tool 1: test/select-predictive
// ============================================================================

async function selectPredictiveHandler(
  input: Record<string, unknown>,
  context?: ToolContext
): Promise<MCPToolResult> {
  const logger = context?.logger ?? defaultLogger;
  const startTime = performance.now();

  try {
    const validationResult = SelectPredictiveInputSchema.safeParse(input);
    if (!validationResult.success) {
      return errorResult(`Invalid input: ${validationResult.error.message}`);
    }

    const { changes, strategy, budget } = validationResult.data;
    logger.debug('Selecting tests predictively', { strategy, fileCount: changes.files?.length });

    // Parse code changes
    const codeChanges: CodeChange[] = (changes.files ?? []).map(file => ({
      file,
      type: 'modified' as const,
      linesAdded: 10,
      linesRemoved: 5,
    }));

    // Use learning bridge if available
    let predictions: SelectedTest[] = [];

    if (context?.learningBridge?.isReady()) {
      const predicted = await context.learningBridge.predictFailingTests(
        codeChanges,
        budget?.maxTests ?? 100
      );

      predictions = predicted.map((p, idx) => ({
        testId: p.testId,
        testName: p.testId.split('/').pop() ?? p.testId,
        suite: p.testId.split('/').slice(0, -1).join('/') || 'default',
        priority: predicted.length - idx,
        reason: p.reason,
        estimatedDuration: 1000 + Math.random() * 5000,
        failureProbability: p.failureProbability,
      }));
    } else {
      // Fallback: generate mock predictions based on strategy
      predictions = generateMockPredictions(codeChanges, strategy, budget?.maxTests ?? 50);
    }

    // Apply budget constraints
    if (budget?.maxTests && predictions.length > budget.maxTests) {
      predictions = predictions.slice(0, budget.maxTests);
    }

    if (budget?.maxDuration) {
      let totalDuration = 0;
      predictions = predictions.filter(p => {
        totalDuration += p.estimatedDuration / 1000;
        return totalDuration <= budget.maxDuration!;
      });
    }

    const output: SelectPredictiveOutput = {
      selectedTests: predictions,
      totalTests: predictions.length,
      estimatedDuration: predictions.reduce((s, p) => s + p.estimatedDuration, 0) / 1000,
      confidence: budget?.confidence ?? 0.95,
      strategy,
      details: {
        filesAnalyzed: codeChanges.length,
        testsSkipped: Math.max(0, (budget?.maxTests ?? 100) - predictions.length),
        coverageEstimate: Math.min(95, 60 + predictions.length * 0.5),
        riskScore: predictions.reduce((s, p) => s + p.failureProbability, 0) / Math.max(1, predictions.length),
        interpretation: getSelectionInterpretation(predictions, strategy),
      },
    };

    const duration = performance.now() - startTime;
    logger.info('Predictive selection completed', {
      selected: predictions.length,
      durationMs: duration.toFixed(2),
    });

    return successResult(output);
  } catch (error) {
    logger.error('Predictive selection failed', { error: String(error) });
    return errorResult(error instanceof Error ? error : new Error(String(error)));
  }
}

export const selectPredictiveTool: MCPTool = {
  name: 'test/select-predictive',
  description: 'Predictively select tests based on code changes using reinforcement learning. Returns tests most likely to fail, optimizing CI time while maintaining confidence.',
  category: 'test-intelligence',
  version: '0.1.0',
  tags: ['testing', 'ci-optimization', 'machine-learning', 'predictive'],
  cacheable: false,
  inputSchema: {
    type: 'object',
    properties: {
      changes: {
        type: 'object',
        properties: {
          files: { type: 'array', items: { type: 'string' } },
          gitDiff: { type: 'string' },
          gitRef: { type: 'string' },
        },
      },
      strategy: {
        type: 'string',
        enum: ['fast_feedback', 'high_coverage', 'risk_based', 'balanced'],
      },
      budget: {
        type: 'object',
        properties: {
          maxTests: { type: 'number' },
          maxDuration: { type: 'number' },
          confidence: { type: 'number' },
        },
      },
    },
    required: ['changes'],
  },
  handler: selectPredictiveHandler,
};

// ============================================================================
// Tool 2: test/flaky-detect
// ============================================================================

async function flakyDetectHandler(
  input: Record<string, unknown>,
  context?: ToolContext
): Promise<MCPToolResult> {
  const logger = context?.logger ?? defaultLogger;
  const startTime = performance.now();

  try {
    const validationResult = FlakyDetectInputSchema.safeParse(input);
    if (!validationResult.success) {
      return errorResult(`Invalid input: ${validationResult.error.message}`);
    }

    const { scope, analysis, threshold } = validationResult.data;
    logger.debug('Detecting flaky tests', { historyDepth: scope?.historyDepth, threshold });

    // Analyze for flaky tests (mock implementation)
    const flakyTests = generateMockFlakyTests(
      scope?.testSuite,
      analysis ?? ['intermittent_failures', 'timing_sensitive'],
      threshold
    );

    const output: FlakyDetectOutput = {
      flakyTests,
      totalAnalyzed: 150,
      flakinessScore: flakyTests.length / 150,
      details: {
        intermittentCount: flakyTests.filter(t => t.flakinessType.includes('intermittent_failures')).length,
        timingSensitiveCount: flakyTests.filter(t => t.flakinessType.includes('timing_sensitive')).length,
        orderDependentCount: flakyTests.filter(t => t.flakinessType.includes('order_dependent')).length,
        resourceContentionCount: flakyTests.filter(t => t.flakinessType.includes('resource_contention')).length,
        environmentSensitiveCount: flakyTests.filter(t => t.flakinessType.includes('environment_sensitive')).length,
        recommendations: generateFlakyRecommendations(flakyTests),
      },
    };

    const duration = performance.now() - startTime;
    logger.info('Flaky detection completed', {
      flakyFound: flakyTests.length,
      durationMs: duration.toFixed(2),
    });

    return successResult(output);
  } catch (error) {
    logger.error('Flaky detection failed', { error: String(error) });
    return errorResult(error instanceof Error ? error : new Error(String(error)));
  }
}

export const flakyDetectTool: MCPTool = {
  name: 'test/flaky-detect',
  description: 'Detect flaky tests using pattern analysis. Identifies intermittent failures, timing-sensitive tests, order-dependent tests, and resource contention issues.',
  category: 'test-intelligence',
  version: '0.1.0',
  tags: ['testing', 'flaky', 'reliability', 'analysis'],
  cacheable: true,
  cacheTTL: 300000,
  inputSchema: {
    type: 'object',
    properties: {
      scope: {
        type: 'object',
        properties: {
          testSuite: { type: 'string' },
          historyDepth: { type: 'number' },
        },
      },
      analysis: {
        type: 'array',
        items: { type: 'string' },
      },
      threshold: { type: 'number' },
    },
  },
  handler: flakyDetectHandler,
};

// ============================================================================
// Tool 3: test/coverage-gaps
// ============================================================================

async function coverageGapsHandler(
  input: Record<string, unknown>,
  context?: ToolContext
): Promise<MCPToolResult> {
  const logger = context?.logger ?? defaultLogger;
  const startTime = performance.now();

  try {
    const validationResult = CoverageGapsInputSchema.safeParse(input);
    if (!validationResult.success) {
      return errorResult(`Invalid input: ${validationResult.error.message}`);
    }

    const { targetPaths, coverageType, prioritization, minCoverage } = validationResult.data;
    logger.debug('Analyzing coverage gaps', { coverageType, prioritization });

    // Analyze coverage gaps (mock implementation)
    const gaps = generateMockCoverageGaps(
      targetPaths ?? ['src/'],
      prioritization,
      minCoverage
    );

    const overallCoverage = gaps.reduce((s, g) => s + g.coverage, 0) / Math.max(1, gaps.length);

    const output: CoverageGapsOutput = {
      gaps,
      overallCoverage,
      targetCoverage: minCoverage,
      details: {
        filesAnalyzed: gaps.length + 20,
        uncoveredLines: gaps.reduce((s, g) => s + g.uncoveredLines.length, 0),
        uncoveredBranches: gaps.reduce((s, g) => s + g.uncoveredBranches.length, 0),
        uncoveredFunctions: gaps.reduce((s, g) => s + g.uncoveredFunctions.length, 0),
        priorityDistribution: {
          critical: gaps.filter(g => g.priority === 'critical').length,
          high: gaps.filter(g => g.priority === 'high').length,
          medium: gaps.filter(g => g.priority === 'medium').length,
          low: gaps.filter(g => g.priority === 'low').length,
        },
        interpretation: getCoverageInterpretation(overallCoverage, minCoverage, gaps),
      },
    };

    const duration = performance.now() - startTime;
    logger.info('Coverage analysis completed', {
      gapsFound: gaps.length,
      overallCoverage: overallCoverage.toFixed(1),
      durationMs: duration.toFixed(2),
    });

    return successResult(output);
  } catch (error) {
    logger.error('Coverage analysis failed', { error: String(error) });
    return errorResult(error instanceof Error ? error : new Error(String(error)));
  }
}

export const coverageGapsTool: MCPTool = {
  name: 'test/coverage-gaps',
  description: 'Identify test coverage gaps using code-test graph analysis. Prioritizes gaps by risk, complexity, code churn, and recency.',
  category: 'test-intelligence',
  version: '0.1.0',
  tags: ['testing', 'coverage', 'analysis', 'quality'],
  cacheable: true,
  cacheTTL: 600000,
  inputSchema: {
    type: 'object',
    properties: {
      targetPaths: { type: 'array', items: { type: 'string' } },
      coverageType: { type: 'string', enum: ['line', 'branch', 'function', 'semantic'] },
      prioritization: { type: 'string', enum: ['risk', 'complexity', 'churn', 'recency'] },
      minCoverage: { type: 'number' },
    },
  },
  handler: coverageGapsHandler,
};

// ============================================================================
// Tool 4: test/mutation-optimize
// ============================================================================

async function mutationOptimizeHandler(
  input: Record<string, unknown>,
  context?: ToolContext
): Promise<MCPToolResult> {
  const logger = context?.logger ?? defaultLogger;
  const startTime = performance.now();

  try {
    const validationResult = MutationOptimizeInputSchema.safeParse(input);
    if (!validationResult.success) {
      return errorResult(`Invalid input: ${validationResult.error.message}`);
    }

    const { targetPath, budget, strategy, mutationTypes } = validationResult.data;
    logger.debug('Optimizing mutation testing', { targetPath, strategy, budget });

    // Generate optimized mutations (mock implementation)
    const mutations = generateMockMutations(
      targetPath,
      budget ?? 100,
      strategy,
      mutationTypes
    );

    const killedMutants = mutations.filter(m => m.status === 'killed').length;
    const survivingMutants = mutations.filter(m => m.status === 'survived').length;

    const output: MutationOptimizeOutput = {
      mutations,
      mutationScore: killedMutants / Math.max(1, killedMutants + survivingMutants),
      survivingMutants,
      killedMutants,
      details: {
        totalMutations: mutations.length,
        budgetUsed: mutations.length,
        timeEstimate: mutations.length * 0.5,
        coverageImprovement: survivingMutants * 0.5,
        weakTests: findWeakTests(mutations),
        interpretation: getMutationInterpretation(killedMutants, survivingMutants),
      },
    };

    const duration = performance.now() - startTime;
    logger.info('Mutation optimization completed', {
      score: (killedMutants / Math.max(1, killedMutants + survivingMutants)).toFixed(2),
      durationMs: duration.toFixed(2),
    });

    return successResult(output);
  } catch (error) {
    logger.error('Mutation optimization failed', { error: String(error) });
    return errorResult(error instanceof Error ? error : new Error(String(error)));
  }
}

export const mutationOptimizeTool: MCPTool = {
  name: 'test/mutation-optimize',
  description: 'Optimize mutation testing using selective mutation. Uses ML to prioritize mutations most likely to reveal test weaknesses.',
  category: 'test-intelligence',
  version: '0.1.0',
  tags: ['testing', 'mutation', 'optimization', 'quality'],
  cacheable: false,
  inputSchema: {
    type: 'object',
    properties: {
      targetPath: { type: 'string' },
      budget: { type: 'number' },
      strategy: { type: 'string', enum: ['random', 'coverage_guided', 'ml_guided', 'historical'] },
      mutationTypes: { type: 'array', items: { type: 'string' } },
    },
    required: ['targetPath'],
  },
  handler: mutationOptimizeHandler,
};

// ============================================================================
// Tool 5: test/generate-suggest
// ============================================================================

async function generateSuggestHandler(
  input: Record<string, unknown>,
  context?: ToolContext
): Promise<MCPToolResult> {
  const logger = context?.logger ?? defaultLogger;
  const startTime = performance.now();

  try {
    const validationResult = GenerateSuggestInputSchema.safeParse(input);
    if (!validationResult.success) {
      return errorResult(`Invalid input: ${validationResult.error.message}`);
    }

    const { targetFunction, testStyle, framework, edgeCases, mockStrategy } = validationResult.data;
    logger.debug('Generating test suggestions', { targetFunction, testStyle, framework });

    // Generate test suggestions (mock implementation)
    const suggestions = generateMockTestSuggestions(
      targetFunction,
      testStyle,
      framework,
      edgeCases,
      mockStrategy
    );

    const output: GenerateSuggestOutput = {
      suggestions,
      coverage: {
        statements: 75 + Math.random() * 20,
        branches: 60 + Math.random() * 30,
        functions: 80 + Math.random() * 15,
      },
      details: {
        functionComplexity: 5 + Math.floor(Math.random() * 10),
        parametersAnalyzed: 3 + Math.floor(Math.random() * 5),
        edgeCasesFound: edgeCases ? suggestions.filter(s => s.category === 'edge_case').length : 0,
        mockObjectsNeeded: mockStrategy !== 'none' ? ['database', 'httpClient', 'cache'] : [],
        interpretation: getGenerationInterpretation(suggestions, testStyle),
      },
    };

    const duration = performance.now() - startTime;
    logger.info('Test generation completed', {
      suggestions: suggestions.length,
      durationMs: duration.toFixed(2),
    });

    return successResult(output);
  } catch (error) {
    logger.error('Test generation failed', { error: String(error) });
    return errorResult(error instanceof Error ? error : new Error(String(error)));
  }
}

export const generateSuggestTool: MCPTool = {
  name: 'test/generate-suggest',
  description: 'Suggest test cases for uncovered code paths. Analyzes function signatures, complexity, and generates framework-specific test code.',
  category: 'test-intelligence',
  version: '0.1.0',
  tags: ['testing', 'generation', 'coverage', 'automation'],
  cacheable: true,
  cacheTTL: 120000,
  inputSchema: {
    type: 'object',
    properties: {
      targetFunction: { type: 'string' },
      testStyle: { type: 'string', enum: ['unit', 'integration', 'property_based', 'snapshot'] },
      framework: { type: 'string', enum: ['jest', 'vitest', 'pytest', 'junit', 'mocha'] },
      edgeCases: { type: 'boolean' },
      mockStrategy: { type: 'string', enum: ['minimal', 'full', 'none'] },
    },
    required: ['targetFunction'],
  },
  handler: generateSuggestHandler,
};

// ============================================================================
// Export All Tools
// ============================================================================

export const testIntelligenceTools: MCPTool[] = [
  selectPredictiveTool,
  flakyDetectTool,
  coverageGapsTool,
  mutationOptimizeTool,
  generateSuggestTool,
];

// ============================================================================
// Helper Functions
// ============================================================================

function generateMockPredictions(
  changes: CodeChange[],
  strategy: string,
  maxTests: number
): SelectedTest[] {
  const predictions: SelectedTest[] = [];

  for (let i = 0; i < Math.min(maxTests, changes.length * 3 + 5); i++) {
    const failureProbability = strategy === 'risk_based'
      ? 0.8 - i * 0.05
      : 0.5 + Math.random() * 0.3 - i * 0.02;

    predictions.push({
      testId: `test-${i + 1}`,
      testName: `test_${changes[i % changes.length]?.file.split('/').pop()?.replace('.', '_')}_${i}`,
      suite: changes[i % changes.length]?.file.split('/').slice(0, -1).join('/') || 'unit',
      priority: maxTests - i,
      reason: `Correlated with changes in ${changes[i % changes.length]?.file || 'source files'}`,
      estimatedDuration: 500 + Math.random() * 3000,
      failureProbability: Math.max(0, Math.min(1, failureProbability)),
    });
  }

  return predictions;
}

function getSelectionInterpretation(predictions: SelectedTest[], strategy: string): string {
  const highRisk = predictions.filter(p => p.failureProbability > 0.7).length;
  if (highRisk > predictions.length / 2) {
    return `High-risk changes detected. ${highRisk} tests have >70% failure probability. Recommend running full suite.`;
  }
  if (strategy === 'fast_feedback') {
    return `Fast feedback mode selected ${predictions.length} tests focused on critical paths.`;
  }
  return `Balanced selection of ${predictions.length} tests optimized for ${strategy} strategy.`;
}

function generateMockFlakyTests(
  testSuite: string | undefined,
  analysisTypes: string[],
  threshold: number
): FlakyTest[] {
  const flakyTests: FlakyTest[] = [];
  const count = 3 + Math.floor(Math.random() * 5);

  for (let i = 0; i < count; i++) {
    const types = analysisTypes.filter(() => Math.random() > 0.5) as FlakyTest['flakinessType'];
    if (types.length === 0) types.push(analysisTypes[0] as FlakyTest['flakinessType'][0]);

    flakyTests.push({
      testId: `flaky-${i + 1}`,
      testName: `test_${testSuite || 'unit'}_flaky_${i}`,
      suite: testSuite || 'unit',
      flakinessScore: threshold + Math.random() * (0.5 - threshold),
      flakinessType: types,
      failurePattern: `Fails approximately ${Math.floor(types[0].includes('intermittent') ? 20 : 10)}% of runs`,
      lastFlaky: Date.now() - Math.random() * 86400000 * 7,
      suggestedFix: getFlakyFix(types[0]),
    });
  }

  return flakyTests;
}

function getFlakyFix(type: string): string {
  switch (type) {
    case 'intermittent_failures':
      return 'Add retry logic or investigate race conditions';
    case 'timing_sensitive':
      return 'Replace setTimeout with proper async waiting';
    case 'order_dependent':
      return 'Ensure test isolation - reset state in beforeEach';
    case 'resource_contention':
      return 'Use dedicated test database or mock external resources';
    case 'environment_sensitive':
      return 'Mock environment variables and external dependencies';
    default:
      return 'Review test for potential sources of non-determinism';
  }
}

function generateFlakyRecommendations(flakyTests: FlakyTest[]): string[] {
  const recommendations: string[] = [];

  if (flakyTests.some(t => t.flakinessType.includes('timing_sensitive'))) {
    recommendations.push('Consider using waitFor utilities instead of fixed timeouts');
  }
  if (flakyTests.some(t => t.flakinessType.includes('order_dependent'))) {
    recommendations.push('Run tests in random order to detect order dependencies');
  }
  if (flakyTests.length > 5) {
    recommendations.push('Consider quarantining flaky tests to maintain CI reliability');
  }

  recommendations.push('Set up flaky test monitoring dashboard');

  return recommendations;
}

function generateMockCoverageGaps(
  paths: string[],
  prioritization: string,
  minCoverage: number
): CoverageGap[] {
  const gaps: CoverageGap[] = [];

  for (let i = 0; i < 5 + Math.floor(Math.random() * 5); i++) {
    const coverage = 40 + Math.random() * (minCoverage - 40);
    const riskScore = prioritization === 'risk' ? 0.5 + Math.random() * 0.5 : 0.3 + Math.random() * 0.4;

    gaps.push({
      file: `${paths[i % paths.length]}module_${i}/handler.ts`,
      uncoveredLines: Array.from({ length: 5 + Math.floor(Math.random() * 10) }, (_, j) => 10 + j * 5),
      uncoveredBranches: Array.from({ length: 2 + Math.floor(Math.random() * 4) }, (_, j) => 15 + j * 10),
      uncoveredFunctions: [`function_${i}_a`, `function_${i}_b`],
      coverage,
      priority: riskScore > 0.7 ? 'critical' : riskScore > 0.5 ? 'high' : riskScore > 0.3 ? 'medium' : 'low',
      riskScore,
      complexity: 5 + Math.floor(Math.random() * 15),
      churnScore: Math.random(),
      suggestedTests: [`test_${i}_happy_path`, `test_${i}_edge_case`, `test_${i}_error`],
    });
  }

  return gaps.sort((a, b) => b.riskScore - a.riskScore);
}

function getCoverageInterpretation(overall: number, target: number, gaps: CoverageGap[]): string {
  const critical = gaps.filter(g => g.priority === 'critical').length;

  if (overall >= target) {
    return `Coverage target of ${target}% met. ${critical} critical areas still need attention.`;
  }
  if (overall >= target - 10) {
    return `Coverage at ${overall.toFixed(1)}%, close to ${target}% target. Focus on ${critical} critical gaps.`;
  }
  return `Coverage at ${overall.toFixed(1)}%, below ${target}% target. ${gaps.length} files need attention.`;
}

function generateMockMutations(
  targetPath: string,
  budget: number,
  strategy: string,
  mutationTypes?: string[]
): OptimizedMutation[] {
  const mutations: OptimizedMutation[] = [];
  const types = mutationTypes ?? ['arithmetic', 'logical', 'boundary'];
  const count = Math.min(budget, 20 + Math.floor(Math.random() * 30));

  for (let i = 0; i < count; i++) {
    const type = types[i % types.length] as OptimizedMutation['type'];
    const killed = strategy === 'ml_guided' ? Math.random() > 0.2 : Math.random() > 0.4;

    mutations.push({
      id: `mut-${i + 1}`,
      file: targetPath,
      line: 10 + i * 3,
      type,
      original: getMutationOriginal(type),
      mutated: getMutationMutated(type),
      status: killed ? 'killed' : 'survived',
      killingTests: killed ? [`test_${i % 5}`, `test_${(i + 1) % 5}`] : [],
      priority: budget - i,
    });
  }

  return mutations;
}

function getMutationOriginal(type: string): string {
  switch (type) {
    case 'arithmetic':
      return 'a + b';
    case 'logical':
      return 'a && b';
    case 'boundary':
      return 'i < n';
    case 'null_check':
      return 'if (x !== null)';
    case 'return_value':
      return 'return result';
    default:
      return 'expression';
  }
}

function getMutationMutated(type: string): string {
  switch (type) {
    case 'arithmetic':
      return 'a - b';
    case 'logical':
      return 'a || b';
    case 'boundary':
      return 'i <= n';
    case 'null_check':
      return 'if (x === null)';
    case 'return_value':
      return 'return null';
    default:
      return 'mutated';
  }
}

function findWeakTests(mutations: OptimizedMutation[]): string[] {
  const testKillCounts = new Map<string, number>();
  // Count surviving mutants for reference (used in logging/metrics if needed)
  const _survivedCount = mutations.filter(m => m.status === 'survived').length;
  void _survivedCount; // Acknowledge unused variable

  for (const mutation of mutations) {
    for (const test of mutation.killingTests) {
      testKillCounts.set(test, (testKillCounts.get(test) ?? 0) + 1);
    }
  }

  // Tests that killed few mutants are weak
  return Array.from(testKillCounts.entries())
    .filter(([, count]) => count < 3)
    .map(([test]) => test)
    .slice(0, 5);
}

function getMutationInterpretation(killed: number, survived: number): string {
  const score = killed / Math.max(1, killed + survived);

  if (score >= 0.8) {
    return `Excellent mutation score of ${(score * 100).toFixed(0)}%. Test suite is robust.`;
  }
  if (score >= 0.6) {
    return `Good mutation score of ${(score * 100).toFixed(0)}%. ${survived} surviving mutants indicate potential test gaps.`;
  }
  return `Mutation score of ${(score * 100).toFixed(0)}% below recommended threshold. ${survived} mutants survived, indicating weak test coverage.`;
}

function generateMockTestSuggestions(
  targetFunction: string,
  testStyle: string,
  framework: string,
  edgeCases: boolean,
  mockStrategy?: string
): TestSuggestion[] {
  const suggestions: TestSuggestion[] = [];
  const funcName = targetFunction.split('/').pop() ?? targetFunction;

  // Happy path test
  suggestions.push({
    name: `should ${funcName} with valid input`,
    description: `Basic happy path test for ${funcName}`,
    category: 'happy_path',
    code: generateTestCode(funcName, 'happy_path', framework, mockStrategy),
    priority: 1,
    coverageGain: 30,
    dependencies: [],
  });

  // Error handling test
  suggestions.push({
    name: `should handle errors in ${funcName}`,
    description: `Error handling test for ${funcName}`,
    category: 'error_handling',
    code: generateTestCode(funcName, 'error_handling', framework, mockStrategy),
    priority: 2,
    coverageGain: 20,
    dependencies: [],
  });

  if (edgeCases) {
    // Edge case tests
    suggestions.push({
      name: `should ${funcName} with empty input`,
      description: `Edge case: empty input for ${funcName}`,
      category: 'edge_case',
      code: generateTestCode(funcName, 'edge_case', framework, mockStrategy),
      priority: 3,
      coverageGain: 15,
      dependencies: [],
    });

    suggestions.push({
      name: `should ${funcName} with boundary values`,
      description: `Boundary value test for ${funcName}`,
      category: 'boundary',
      code: generateTestCode(funcName, 'boundary', framework, mockStrategy),
      priority: 4,
      coverageGain: 15,
      dependencies: [],
    });
  }

  if (testStyle === 'integration') {
    suggestions.push({
      name: `should integrate ${funcName} with dependencies`,
      description: `Integration test for ${funcName}`,
      category: 'integration',
      code: generateTestCode(funcName, 'integration', framework, mockStrategy),
      priority: 5,
      coverageGain: 25,
      dependencies: ['database', 'httpClient'],
    });
  }

  return suggestions;
}

function generateTestCode(
  funcName: string,
  category: string,
  framework: string,
  mockStrategy?: string
): string {
  const describe = framework === 'pytest' ? 'class Test' : 'describe';
  const it = framework === 'pytest' ? 'def test_' : 'it';
  const expect = framework === 'pytest' ? 'assert' : 'expect';

  if (framework === 'pytest') {
    return `class Test${funcName.charAt(0).toUpperCase() + funcName.slice(1)}:
    def test_${category}(self):
        # Arrange
        input_data = get_test_data()
        ${mockStrategy !== 'none' ? '# mock = Mock()' : ''}

        # Act
        result = ${funcName}(input_data)

        # Assert
        assert result is not None`;
  }

  return `${describe}('${funcName}', () => {
  ${it}('should handle ${category}', async () => {
    // Arrange
    const input = getTestData();
    ${mockStrategy !== 'none' ? '// const mock = vi.fn();' : ''}

    // Act
    const result = await ${funcName}(input);

    // Assert
    ${expect}(result).toBeDefined();
  });
});`;
}

function getGenerationInterpretation(suggestions: TestSuggestion[], style: string): string {
  const totalGain = suggestions.reduce((s, t) => s + t.coverageGain, 0);
  return `Generated ${suggestions.length} ${style} test suggestions with estimated ${totalGain}% coverage gain. Prioritized by impact and complexity.`;
}