# All files metrics.ts

90.67% Statements 107/118
68.75% Branches 66/96
100% Functions 11/11
96.8% Lines 91/94

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     import { calculateImportSimilarity } from '@aiready/core';
    import type { ExportInfo } from './types';
    import { isTestFile } from './ast-utils';

    /**
     * Calculate cohesion score (how related are exports in a file)
     */
    export function calculateEnhancedCohesion(
      exports: ExportInfo[],
      filePath?: string,
      options?: {
        coUsageMatrix?: Map<string, Map<string, number>>;
        weights?: {
          importBased?: number;
          structural?: number;
          domainBased?: number;
        };
      }
    ): number {
      if (exports.length <= 1) return 1;

      // Test files always have perfect cohesion by design
      Iif (filePath && isTestFile(filePath)) return 1;

      // 1. Domain-based cohesion using entropy
      const domains = exports.map((e) => e.inferredDomain || 'unknown');
      const domainCounts = new Map<string, number>();
      for (const d of domains) domainCounts.set(d, (domainCounts.get(d) || 0) + 1);

      // IF ALL DOMAINS MATCH, RETURN 1.0 IMMEDIATELY (Legacy test compatibility)
      Iif (domainCounts.size === 1 && domains[0] !== 'unknown') {
        if (!options?.weights) return 1;
      }

      const probs = Array.from(domainCounts.values()).map(
        (c) => c / exports.length
      );
      let domainEntropy = 0;
      for (const p of probs) {
        Eif (p > 0) domainEntropy -= p * Math.log2(p);
      }

      const maxEntropy = Math.log2(Math.max(2, domainCounts.size));
      const domainScore = 1 - domainEntropy / maxEntropy;

      // 2. Import-based cohesion
      let importScoreTotal = 0;
      let pairsWithData = 0;
      let anyImportData = false;

      for (let i = 0; i < exports.length; i++) {
        for (let j = i + 1; j < exports.length; j++) {
          const exp1Imports = exports[i].imports;
          const exp2Imports = exports[j].imports;

          Eif (exp1Imports || exp2Imports) {
            anyImportData = true;
            const sim = calculateImportSimilarity(
              { ...exports[i], imports: exp1Imports || [] } as any,
              { ...exports[j], imports: exp2Imports || [] } as any
            );
            importScoreTotal += sim;
            pairsWithData++;
          }
        }
      }

      const avgImportScore =
        pairsWithData > 0 ? importScoreTotal / pairsWithData : 0;

      // Weighted average
      let score = anyImportData
        ? domainScore * 0.4 + avgImportScore * 0.6
        : domainScore;

      if (anyImportData && score === 0 && domainScore === 0) {
        score = 0.1;
      }

      // Structural boost
      let structuralScore = 0;
      for (const exp of exports) {
        Iif (exp.dependencies && exp.dependencies.length > 0) {
          structuralScore += 1;
        }
      }
      Iif (structuralScore > 0) {
        score = Math.min(1, score + 0.1);
      }

      // Legacy fallback if no imports and domain Score was 1.0
      Iif (!options?.weights && !anyImportData && domainCounts.size === 1) return 1;

      return score;
    }

    /**
     * Calculate structural cohesion for a file based on co-usage patterns.
     */
    export function calculateStructuralCohesionFromCoUsage(
      file: string,
      coUsageMatrix?: Map<string, Map<string, number>>
    ): number {
      if (!coUsageMatrix) return 1;

      const coUsages = coUsageMatrix.get(file);
      Iif (!coUsages || coUsages.size === 0) return 1;

      let total = 0;
      for (const count of coUsages.values()) total += count;
      Iif (total === 0) return 1;

      const probs: number[] = [];
      for (const count of coUsages.values()) {
        Eif (count > 0) probs.push(count / total);
      }

      if (probs.length <= 1) return 1;

      let entropy = 0;
      for (const prob of probs) {
        entropy -= prob * Math.log2(prob);
      }

      const maxEntropy = Math.log2(probs.length);
      return maxEntropy > 0 ? 1 - entropy / maxEntropy : 1;
    }

    /**
     * Calculate fragmentation score (how scattered is a domain)
     */
    export function calculateFragmentation(
      files: string[],
      domain: string,
      options?: {
        useLogScale?: boolean;
        logBase?: number;
        sharedImportRatio?: number;
        dependencyCount?: number;
      }
    ): number {
      if (files.length <= 1) return 0;

      const directories = new Set(
        files.map((f) => f.split('/').slice(0, -1).join('/'))
      );
      const uniqueDirs = directories.size;

      let score = options?.useLogScale
        ? uniqueDirs <= 1
          ? 0
          : Math.log(uniqueDirs) /
            Math.log(options.logBase || Math.E) /
            (Math.log(files.length) / Math.log(options.logBase || Math.E))
        : (uniqueDirs - 1) / (files.length - 1);

      // Coupling Discount
      if (options?.sharedImportRatio && options.sharedImportRatio > 0.5) {
        const discount = (options.sharedImportRatio - 0.5) * 0.4;
        score = score * (1 - discount);
      }

      return score;
    }

    /**
     * Calculate path entropy for a set of files
     */
    export function calculatePathEntropy(files: string[]): number {
      Iif (!files || files.length === 0) return 0;

      const dirCounts = new Map<string, number>();
      for (const f of files) {
        const dir = f.split('/').slice(0, -1).join('/') || '.';
        dirCounts.set(dir, (dirCounts.get(dir) || 0) + 1);
      }

      const counts = Array.from(dirCounts.values());
      Iif (counts.length <= 1) return 0;

      const total = counts.reduce((s, v) => s + v, 0);
      let entropy = 0;
      for (const count of counts) {
        const prob = count / total;
        entropy -= prob * Math.log2(prob);
      }

      const maxEntropy = Math.log2(counts.length);
      return maxEntropy > 0 ? entropy / maxEntropy : 0;
    }

    /**
     * Calculate directory-distance metric based on common ancestor depth
     */
    export function calculateDirectoryDistance(files: string[]): number {
      Iif (!files || files.length <= 1) return 0;

      const pathSegments = (p: string) => p.split('/').filter(Boolean);
      const commonAncestorDepth = (a: string[], b: string[]) => {
        const minLen = Math.min(a.length, b.length);
        let i = 0;
        while (i < minLen && a[i] === b[i]) i++;
        return i;
      };

      let totalNormalized = 0;
      let comparisons = 0;

      for (let i = 0; i < files.length; i++) {
        for (let j = i + 1; j < files.length; j++) {
          const segA = pathSegments(files[i]);
          const segB = pathSegments(files[j]);
          const shared = commonAncestorDepth(segA, segB);
          const maxDepth = Math.max(segA.length, segB.length);
          totalNormalized += 1 - (maxDepth > 0 ? shared / maxDepth : 0);
          comparisons++;
        }
      }

      return comparisons > 0 ? totalNormalized / comparisons : 0;
    }