claude-playwright/dist/core/bidirectional-cache.cjs.map

Seamless integration between Claude Code and Playwright MCP for efficient browser automation and testing

{"version":3,"sources":["../../src/core/bidirectional-cache.ts","../../src/core/smart-normalizer.ts"],"sourcesContent":["import Database from 'better-sqlite3';\nimport { SmartNormalizer, NormalizationResult } from './smart-normalizer.js';\nimport crypto from 'crypto';\nimport * as path from 'path';\nimport * as os from 'os';\nimport * as fs from 'fs';\n\ninterface SelectorCacheEntry {\n  id?: number;\n  selector: string;\n  selector_hash: string;\n  url: string;\n  confidence: number;\n  created_at: number;\n  last_used: number;\n  use_count: number;\n}\n\ninterface InputMappingEntry {\n  id?: number;\n  selector_hash: string;\n  input: string;\n  normalized_input: string;\n  input_tokens: string; // JSON array\n  url: string;\n  success_count: number;\n  last_used: number;\n  confidence: number;\n  learned_from: 'direct' | 'inferred' | 'pattern';\n}\n\ninterface CacheOptions {\n  maxSizeMB?: number;\n  selectorTTL?: number;\n  cleanupInterval?: number;\n  maxVariationsPerSelector?: number;\n}\n\ninterface LookupResult {\n  selector: string;\n  confidence: number;\n  source: 'exact' | 'normalized' | 'reverse' | 'fuzzy';\n  cached: boolean;\n}\n\nexport class BidirectionalCache {\n  private db: Database.Database;\n  private normalizer: SmartNormalizer;\n  private cleanupTimer?: NodeJS.Timeout;\n  private options: Required<CacheOptions>;\n  private cacheDir: string;\n  private stats = {\n    hits: { exact: 0, normalized: 0, reverse: 0, fuzzy: 0 },\n    misses: 0,\n    sets: 0,\n    learnings: 0\n  };\n\n  constructor(options: CacheOptions = {}) {\n    this.options = {\n      maxSizeMB: options.maxSizeMB ?? 50,\n      selectorTTL: options.selectorTTL ?? 300000, // 5 minutes\n      cleanupInterval: options.cleanupInterval ?? 60000, // 1 minute\n      maxVariationsPerSelector: options.maxVariationsPerSelector ?? 20\n    };\n\n    this.normalizer = new SmartNormalizer();\n    \n    // Create cache directory\n    this.cacheDir = path.join(os.homedir(), '.claude-playwright', 'cache');\n    if (!fs.existsSync(this.cacheDir)) {\n      fs.mkdirSync(this.cacheDir, { recursive: true });\n    }\n\n    // Initialize database\n    const dbPath = path.join(this.cacheDir, 'bidirectional-cache.db');\n    this.db = new Database(dbPath);\n    this.db.pragma('journal_mode = WAL');\n    this.db.pragma('synchronous = NORMAL');\n    \n    this.initializeDatabase();\n    this.startCleanupTimer();\n  }\n\n  private initializeDatabase(): void {\n    this.db.exec(`\n      -- Enhanced selector cache table\n      CREATE TABLE IF NOT EXISTS selector_cache_v2 (\n        id INTEGER PRIMARY KEY AUTOINCREMENT,\n        selector TEXT NOT NULL,\n        selector_hash TEXT NOT NULL UNIQUE,\n        url TEXT NOT NULL,\n        confidence REAL DEFAULT 0.5,\n        created_at INTEGER NOT NULL,\n        last_used INTEGER NOT NULL,\n        use_count INTEGER DEFAULT 1\n      );\n\n      -- Bidirectional input mappings\n      CREATE TABLE IF NOT EXISTS input_mappings (\n        id INTEGER PRIMARY KEY AUTOINCREMENT,\n        selector_hash TEXT NOT NULL,\n        input TEXT NOT NULL,\n        normalized_input TEXT NOT NULL,\n        input_tokens TEXT NOT NULL,\n        url TEXT NOT NULL,\n        success_count INTEGER DEFAULT 1,\n        last_used INTEGER NOT NULL,\n        confidence REAL DEFAULT 0.5,\n        learned_from TEXT DEFAULT 'direct',\n        FOREIGN KEY (selector_hash) REFERENCES selector_cache_v2(selector_hash),\n        UNIQUE(selector_hash, normalized_input, url)\n      );\n\n      -- Performance indices\n      CREATE INDEX IF NOT EXISTS idx_selector_hash_v2 ON selector_cache_v2(selector_hash);\n      CREATE INDEX IF NOT EXISTS idx_url_v2 ON selector_cache_v2(url);\n      CREATE INDEX IF NOT EXISTS idx_input_normalized ON input_mappings(normalized_input, url);\n      CREATE INDEX IF NOT EXISTS idx_mapping_selector_hash ON input_mappings(selector_hash);\n      CREATE INDEX IF NOT EXISTS idx_url_selector ON input_mappings(url, selector_hash);\n      CREATE INDEX IF NOT EXISTS idx_tokens ON input_mappings(input_tokens);\n\n      -- Migration: Copy from old cache if exists\n      INSERT OR IGNORE INTO selector_cache_v2 (selector, selector_hash, url, confidence, created_at, last_used)\n      SELECT data as selector, \n             substr(cache_key, 1, 32) as selector_hash,\n             url,\n             0.5 as confidence,\n             created_at,\n             accessed_at as last_used\n      FROM cache \n      WHERE cache_type = 'selector' \n      AND NOT EXISTS (SELECT 1 FROM selector_cache_v2 WHERE selector_hash = substr(cache.cache_key, 1, 32));\n    `);\n  }\n\n  async set(input: string, url: string, selector: string): Promise<void> {\n    const now = Date.now();\n    const selectorHash = this.createSelectorHash(selector);\n    const normalizedResult = this.normalizer.normalize(input);\n\n    try {\n      // Begin transaction\n      const transaction = this.db.transaction(() => {\n        // 1. Store/update selector\n        const selectorStmt = this.db.prepare(`\n          INSERT INTO selector_cache_v2 \n          (selector, selector_hash, url, confidence, created_at, last_used, use_count)\n          VALUES (?, ?, ?, 0.5, ?, ?, 1)\n          ON CONFLICT(selector_hash) DO UPDATE SET\n            last_used = excluded.last_used,\n            use_count = use_count + 1,\n            confidence = MIN(confidence * 1.02, 1.0)\n        `);\n        \n        selectorStmt.run(selector, selectorHash, url, now, now);\n\n        // 2. Store input mapping\n        const mappingStmt = this.db.prepare(`\n          INSERT INTO input_mappings \n          (selector_hash, input, normalized_input, input_tokens, url, last_used, learned_from)\n          VALUES (?, ?, ?, ?, ?, ?, 'direct')\n          ON CONFLICT(selector_hash, normalized_input, url) DO UPDATE SET\n            success_count = success_count + 1,\n            confidence = MIN(confidence * 1.05, 1.0),\n            last_used = excluded.last_used,\n            input = CASE \n              WHEN length(excluded.input) > length(input) THEN excluded.input \n              ELSE input \n            END\n        `);\n\n        mappingStmt.run(\n          selectorHash,\n          input,\n          normalizedResult.normalized,\n          JSON.stringify(normalizedResult.tokens),\n          url,\n          now\n        );\n      });\n\n      transaction();\n      this.stats.sets++;\n\n      // 3. Learn from related inputs (async, non-blocking)\n      setImmediate(() => this.learnRelatedInputs(selectorHash, input, url, normalizedResult));\n\n    } catch (error) {\n      console.error('[BidirectionalCache] Set error:', error);\n    }\n  }\n\n  async get(input: string, url: string): Promise<LookupResult | null> {\n    const normalizedResult = this.normalizer.normalize(input);\n\n    // Level 1: Exact Match\n    const exactResult = await this.exactMatch(input, url);\n    if (exactResult) {\n      this.stats.hits.exact++;\n      return { ...exactResult, source: 'exact', cached: true };\n    }\n\n    // Level 2: Normalized Match\n    const normalizedMatch = await this.normalizedMatch(normalizedResult.normalized, url);\n    if (normalizedMatch) {\n      this.stats.hits.normalized++;\n      return { ...normalizedMatch, source: 'normalized', cached: true };\n    }\n\n    // Level 3: Reverse Lookup\n    const reverseMatch = await this.reverseLookup(normalizedResult, url);\n    if (reverseMatch) {\n      this.stats.hits.reverse++;\n      return { ...reverseMatch, source: 'reverse', cached: true };\n    }\n\n    // Level 4: Fuzzy Match (typo tolerance)\n    const fuzzyMatch = await this.fuzzyMatch(normalizedResult, url);\n    if (fuzzyMatch) {\n      this.stats.hits.fuzzy++;\n      return { ...fuzzyMatch, source: 'fuzzy', cached: true };\n    }\n\n    this.stats.misses++;\n    return null;\n  }\n\n  private async exactMatch(input: string, url: string): Promise<{ selector: string; confidence: number } | null> {\n    try {\n      const stmt = this.db.prepare(`\n        SELECT sc.selector, im.confidence\n        FROM input_mappings im\n        JOIN selector_cache_v2 sc ON sc.selector_hash = im.selector_hash\n        WHERE im.input = ? AND im.url = ?\n        ORDER BY im.confidence DESC, im.success_count DESC\n        LIMIT 1\n      `);\n\n      const result = stmt.get(input, url) as { selector: string; confidence: number } | undefined;\n      if (result) {\n        await this.updateUsage(result.selector, url);\n        return result;\n      }\n    } catch (error) {\n      console.error('[BidirectionalCache] Exact match error:', error);\n    }\n    return null;\n  }\n\n  private async normalizedMatch(normalized: string, url: string): Promise<{ selector: string; confidence: number } | null> {\n    try {\n      const stmt = this.db.prepare(`\n        SELECT sc.selector, im.confidence\n        FROM input_mappings im\n        JOIN selector_cache_v2 sc ON sc.selector_hash = im.selector_hash\n        WHERE im.normalized_input = ? AND im.url = ?\n        ORDER BY im.confidence DESC, im.success_count DESC\n        LIMIT 1\n      `);\n\n      const result = stmt.get(normalized, url) as { selector: string; confidence: number } | undefined;\n      if (result) {\n        await this.updateUsage(result.selector, url);\n        return result;\n      }\n    } catch (error) {\n      console.error('[BidirectionalCache] Normalized match error:', error);\n    }\n    return null;\n  }\n\n  private async reverseLookup(normalizedResult: NormalizationResult, url: string): Promise<{ selector: string; confidence: number } | null> {\n    try {\n      // Find selectors with similar token patterns\n      const stmt = this.db.prepare(`\n        SELECT \n          sc.selector,\n          im.confidence,\n          im.input_tokens,\n          im.success_count,\n          GROUP_CONCAT(im.input) as all_inputs\n        FROM input_mappings im\n        JOIN selector_cache_v2 sc ON sc.selector_hash = im.selector_hash\n        WHERE im.url = ?\n        AND json_array_length(im.input_tokens) > 0\n        GROUP BY sc.selector_hash\n        ORDER BY im.confidence DESC, im.success_count DESC\n        LIMIT 10\n      `);\n\n      const candidates = stmt.all(url) as Array<{\n        selector: string;\n        confidence: number;\n        input_tokens: string;\n        success_count: number;\n        all_inputs: string;\n      }>;\n\n      let bestMatch: { selector: string; confidence: number } | null = null;\n      let bestScore = 0;\n\n      for (const candidate of candidates) {\n        try {\n          const candidateTokens = JSON.parse(candidate.input_tokens);\n          const similarity = this.calculateJaccardSimilarity(\n            normalizedResult.tokens,\n            candidateTokens\n          );\n\n          // Boost score based on success count and confidence\n          const boostedScore = similarity * \n            (1 + Math.log(1 + candidate.success_count) * 0.1) *\n            candidate.confidence;\n\n          if (boostedScore > 0.6 && boostedScore > bestScore) {\n            bestScore = boostedScore;\n            bestMatch = {\n              selector: candidate.selector,\n              confidence: candidate.confidence * 0.9 // Slight penalty for reverse lookup\n            };\n          }\n        } catch (e) {\n          // Skip malformed JSON\n          continue;\n        }\n      }\n\n      if (bestMatch) {\n        await this.updateUsage(bestMatch.selector, url);\n        return bestMatch;\n      }\n    } catch (error) {\n      console.error('[BidirectionalCache] Reverse lookup error:', error);\n    }\n    return null;\n  }\n\n  private async fuzzyMatch(normalizedResult: NormalizationResult, url: string): Promise<{ selector: string; confidence: number } | null> {\n    try {\n      const stmt = this.db.prepare(`\n        SELECT sc.selector, im.normalized_input, im.confidence\n        FROM input_mappings im\n        JOIN selector_cache_v2 sc ON sc.selector_hash = im.selector_hash\n        WHERE im.url = ?\n        AND im.last_used > ?\n        ORDER BY im.confidence DESC, im.success_count DESC\n        LIMIT 20\n      `);\n\n      const candidates = stmt.all(url, Date.now() - 3600000) as Array<{ // Last hour only\n        selector: string;\n        normalized_input: string;\n        confidence: number;\n      }>;\n\n      for (const candidate of candidates) {\n        const distance = this.normalizer.damerauLevenshtein(\n          normalizedResult.normalized,\n          candidate.normalized_input\n        );\n\n        const maxDistance = Math.floor(normalizedResult.normalized.length / 8); // 12.5% tolerance\n        \n        if (distance <= maxDistance && distance > 0) {\n          await this.updateUsage(candidate.selector, url);\n          return {\n            selector: candidate.selector,\n            confidence: candidate.confidence * (1 - distance / 10) // Penalty for typos\n          };\n        }\n      }\n    } catch (error) {\n      console.error('[BidirectionalCache] Fuzzy match error:', error);\n    }\n    return null;\n  }\n\n  private calculateJaccardSimilarity(set1: string[], set2: string[]): number {\n    const intersection = set1.filter(x => set2.includes(x));\n    const union = [...new Set([...set1, ...set2])];\n    return intersection.length / union.length;\n  }\n\n  private createSelectorHash(selector: string): string {\n    return crypto.createHash('md5').update(selector).digest('hex');\n  }\n\n  private async updateUsage(selector: string, url: string): Promise<void> {\n    try {\n      const now = Date.now();\n      const selectorHash = this.createSelectorHash(selector);\n      \n      const stmt = this.db.prepare(`\n        UPDATE selector_cache_v2 \n        SET last_used = ?, use_count = use_count + 1\n        WHERE selector_hash = ? AND url = ?\n      `);\n      \n      stmt.run(now, selectorHash, url);\n    } catch (error) {\n      console.error('[BidirectionalCache] Update usage error:', error);\n    }\n  }\n\n  private async learnRelatedInputs(\n    selectorHash: string, \n    newInput: string, \n    url: string, \n    normalizedResult: NormalizationResult\n  ): Promise<void> {\n    try {\n      // Find other inputs for the same selector\n      const stmt = this.db.prepare(`\n        SELECT input, normalized_input, input_tokens, confidence\n        FROM input_mappings\n        WHERE selector_hash = ? AND url = ? AND input != ?\n        AND success_count > 1\n        ORDER BY confidence DESC\n        LIMIT 5\n      `);\n\n      const related = stmt.all(selectorHash, url, newInput) as Array<{\n        input: string;\n        normalized_input: string;\n        input_tokens: string;\n        confidence: number;\n      }>;\n\n      for (const rel of related) {\n        const pattern = this.findCommonPattern(newInput, rel.input);\n        if (pattern && pattern.confidence > 0.7) {\n          await this.saveLearnedPattern(pattern, selectorHash, url);\n          this.stats.learnings++;\n        }\n      }\n    } catch (error) {\n      console.error('[BidirectionalCache] Learn related inputs error:', error);\n    }\n  }\n\n  private findCommonPattern(input1: string, input2: string): { pattern: string; confidence: number } | null {\n    // Simple pattern extraction - can be enhanced\n    const norm1 = this.normalizer.normalize(input1);\n    const norm2 = this.normalizer.normalize(input2);\n    \n    const commonTokens = norm1.tokens.filter(t => norm2.tokens.includes(t));\n    if (commonTokens.length >= 2) {\n      return {\n        pattern: commonTokens.sort().join(' '),\n        confidence: commonTokens.length / Math.max(norm1.tokens.length, norm2.tokens.length)\n      };\n    }\n    \n    return null;\n  }\n\n  private async saveLearnedPattern(\n    pattern: { pattern: string; confidence: number },\n    selectorHash: string,\n    url: string\n  ): Promise<void> {\n    try {\n      const now = Date.now();\n      const stmt = this.db.prepare(`\n        INSERT OR IGNORE INTO input_mappings\n        (selector_hash, input, normalized_input, input_tokens, url, last_used, confidence, learned_from)\n        VALUES (?, ?, ?, ?, ?, ?, ?, 'pattern')\n      `);\n\n      stmt.run(\n        selectorHash,\n        `Pattern: ${pattern.pattern}`,\n        pattern.pattern,\n        JSON.stringify(pattern.pattern.split(' ')),\n        url,\n        now,\n        pattern.confidence,\n      );\n    } catch (error) {\n      // Ignore conflicts - pattern might already exist\n    }\n  }\n\n  private startCleanupTimer(): void {\n    this.cleanupTimer = setInterval(() => {\n      this.cleanup();\n    }, this.options.cleanupInterval);\n  }\n\n  private cleanup(): void {\n    try {\n      const now = Date.now();\n      \n      // Remove expired entries\n      const expiredStmt = this.db.prepare(`\n        DELETE FROM input_mappings \n        WHERE (last_used + ?) < ?\n      `);\n      expiredStmt.run(this.options.selectorTTL, now);\n\n      // Limit variations per selector\n      const limitStmt = this.db.prepare(`\n        DELETE FROM input_mappings\n        WHERE id NOT IN (\n          SELECT id FROM (\n            SELECT id, ROW_NUMBER() OVER (\n              PARTITION BY selector_hash, url \n              ORDER BY confidence DESC, success_count DESC, last_used DESC\n            ) as rn\n            FROM input_mappings\n          ) WHERE rn <= ?\n        )\n      `);\n      limitStmt.run(this.options.maxVariationsPerSelector);\n\n      // Clean orphaned selectors\n      const orphanStmt = this.db.prepare(`\n        DELETE FROM selector_cache_v2\n        WHERE selector_hash NOT IN (\n          SELECT DISTINCT selector_hash FROM input_mappings\n        )\n      `);\n      orphanStmt.run();\n\n    } catch (error) {\n      console.error('[BidirectionalCache] Cleanup error:', error);\n    }\n  }\n\n  async getStats(): Promise<any> {\n    try {\n      const dbStats = this.db.prepare(`\n        SELECT \n          COUNT(DISTINCT sc.selector_hash) as unique_selectors,\n          COUNT(im.id) as total_mappings,\n          AVG(im.success_count) as avg_success_count,\n          COUNT(im.id) * 1.0 / COUNT(DISTINCT sc.selector_hash) as avg_inputs_per_selector,\n          SUM(CASE WHEN im.learned_from = 'inferred' THEN 1 ELSE 0 END) * 100.0 / COUNT(im.id) as learning_rate\n        FROM input_mappings im\n        JOIN selector_cache_v2 sc ON sc.selector_hash = im.selector_hash\n      `).get();\n\n      const hitRate = Object.values(this.stats.hits).reduce((a, b) => a + b, 0) / \n                     (Object.values(this.stats.hits).reduce((a, b) => a + b, 0) + this.stats.misses);\n\n      return {\n        performance: {\n          hitRate: hitRate || 0,\n          hits: this.stats.hits,\n          misses: this.stats.misses,\n          totalLookups: Object.values(this.stats.hits).reduce((a, b) => a + b, 0) + this.stats.misses\n        },\n        storage: dbStats,\n        operations: {\n          sets: this.stats.sets,\n          learnings: this.stats.learnings\n        }\n      };\n    } catch (error) {\n      console.error('[BidirectionalCache] Get stats error:', error);\n      return {};\n    }\n  }\n\n  async clear(): Promise<void> {\n    try {\n      this.db.exec('DELETE FROM input_mappings');\n      this.db.exec('DELETE FROM selector_cache_v2');\n      \n      // Reset stats\n      this.stats = {\n        hits: { exact: 0, normalized: 0, reverse: 0, fuzzy: 0 },\n        misses: 0,\n        sets: 0,\n        learnings: 0\n      };\n    } catch (error) {\n      console.error('[BidirectionalCache] Clear error:', error);\n    }\n  }\n\n  close(): void {\n    if (this.cleanupTimer) {\n      clearInterval(this.cleanupTimer);\n    }\n    this.db.close();\n  }\n}","import crypto from 'crypto';\n\ninterface PositionalKeyword {\n  word: string;\n  position: number;\n  context?: string;\n}\n\ninterface InputFeatures {\n  hasId: boolean;\n  hasClass: boolean;\n  hasQuoted: boolean;\n  numbers: string[];\n  positions: PositionalKeyword[];\n  attributes: string[];\n  wordCount: number;\n  hasImperative: boolean;\n  casePattern: 'lower' | 'upper' | 'mixed' | 'title';\n  isNavigation: boolean;\n  isFormAction: boolean;\n  hasDataTestId: boolean;\n}\n\nexport interface NormalizationResult {\n  normalized: string;\n  tokens: string[];\n  positions: PositionalKeyword[];\n  features: InputFeatures;\n  hash: string;\n}\n\nexport class SmartNormalizer {\n  private readonly POSITION_KEYWORDS = [\n    'before', 'after', 'first', 'last', 'next', 'previous', \n    'above', 'below', 'top', 'bottom', 'left', 'right'\n  ];\n  \n  private readonly RELATION_KEYWORDS = [\n    'in', 'of', 'from', 'to', 'with', 'by', 'for'\n  ];\n  \n  private readonly STOP_WORDS = [\n    'the', 'a', 'an', 'and', 'or', 'but', 'at', 'on'\n  ];\n  \n  private readonly ACTION_SYNONYMS = {\n    'click': ['click', 'press', 'tap', 'hit', 'select', 'choose'],\n    'type': ['type', 'enter', 'input', 'fill', 'write'],\n    'navigate': ['go', 'navigate', 'open', 'visit', 'load'],\n    'hover': ['hover', 'mouseover', 'move']\n  };\n\n  normalize(input: string): NormalizationResult {\n    const original = input.trim();\n    const features = this.extractFeatures(original);\n    \n    // Step 1: Basic cleanup\n    let text = original.toLowerCase();\n    \n    // Step 2: Extract and preserve quoted strings\n    const quotedStrings: string[] = [];\n    text = text.replace(/([\"'])((?:(?!\\1)[^\\\\]|\\\\.)*)(\\1)/g, (match, quote, content) => {\n      quotedStrings.push(content);\n      return `QUOTED_${quotedStrings.length - 1}`;\n    });\n    \n    // Step 3: Extract positional information\n    const positions = this.extractPositions(text);\n    \n    // Step 4: Normalize actions to canonical forms\n    text = this.normalizeActions(text);\n    \n    // Step 5: Remove common patterns\n    text = this.removeCommonPatterns(text);\n    \n    // Step 6: Tokenize and filter\n    const words = text.split(/\\s+/).filter(word => word.length > 0);\n    const tokens = [];\n    const preserved = [];\n    \n    for (let i = 0; i < words.length; i++) {\n      const word = words[i];\n      \n      if (this.POSITION_KEYWORDS.includes(word)) {\n        // Preserve positional keywords with context\n        preserved.push({\n          word,\n          position: i,\n          context: words[i + 1] || null\n        });\n      } else if (!this.STOP_WORDS.includes(word) && \n                 !this.RELATION_KEYWORDS.includes(word) &&\n                 !['button', 'field', 'element'].includes(word)) {\n        tokens.push(word);\n      }\n    }\n    \n    // Step 7: Sort tokens for order-invariance (except preserved)\n    tokens.sort();\n    \n    // Step 8: Build normalized string\n    let normalized = tokens.join(' ');\n    \n    // Step 9: Add positional information\n    if (preserved.length > 0) {\n      const posInfo = preserved.map(p => \n        `${p.word}${p.context ? '-' + p.context : ''}`\n      ).join(',');\n      normalized += ` _pos:${posInfo}`;\n    }\n    \n    // Step 10: Add quoted content back\n    if (quotedStrings.length > 0) {\n      normalized += ` _quoted:${quotedStrings.join(',')}`;\n    }\n    \n    const hash = crypto.createHash('md5').update(normalized).digest('hex');\n    \n    return {\n      normalized,\n      tokens,\n      positions,\n      features,\n      hash\n    };\n  }\n\n  extractFeatures(input: string): InputFeatures {\n    const text = input.toLowerCase();\n    \n    return {\n      hasId: /#[\\w-]+/.test(input),\n      hasClass: /\\.[\\w-]+/.test(input),\n      hasQuoted: /\"[^\"]+\"|'[^']+'/.test(input),\n      numbers: (input.match(/\\d+/g) || []),\n      positions: this.extractPositions(text),\n      attributes: this.extractAttributes(input),\n      wordCount: input.split(/\\s+/).length,\n      hasImperative: /^(click|press|tap|select|enter|type|fill)/i.test(input),\n      casePattern: this.detectCasePattern(input),\n      isNavigation: /^(go|navigate|open|visit)/i.test(input),\n      isFormAction: /(submit|enter|fill|type|input)/i.test(input),\n      hasDataTestId: /data-test|testid|data-cy/i.test(input)\n    };\n  }\n\n  private extractPositions(text: string): PositionalKeyword[] {\n    const positions: PositionalKeyword[] = [];\n    const words = text.split(/\\s+/);\n    \n    for (let i = 0; i < words.length; i++) {\n      const word = words[i];\n      if (this.POSITION_KEYWORDS.includes(word)) {\n        positions.push({\n          word,\n          position: i,\n          context: words[i + 1] || words[i - 1] || undefined\n        });\n      }\n    }\n    \n    return positions;\n  }\n\n  private extractAttributes(input: string): string[] {\n    const attributes = [];\n    \n    // Extract common attribute patterns\n    const patterns = [\n      /\\[([^\\]]+)\\]/g,  // [attribute=value]\n      /data-[\\w-]+/g,   // data-testid\n      /aria-[\\w-]+/g,   // aria-label\n      /role=\"[\\w-]+\"/g, // role=\"button\"\n      /type=\"[\\w-]+\"/g, // type=\"submit\"\n      /placeholder=\"[^\"]+\"/g // placeholder=\"text\"\n    ];\n    \n    for (const pattern of patterns) {\n      const matches = input.match(pattern);\n      if (matches) {\n        attributes.push(...matches);\n      }\n    }\n    \n    return attributes;\n  }\n\n  private detectCasePattern(input: string): 'lower' | 'upper' | 'mixed' | 'title' {\n    const hasLower = /[a-z]/.test(input);\n    const hasUpper = /[A-Z]/.test(input);\n    \n    if (!hasLower && hasUpper) return 'upper';\n    if (hasLower && !hasUpper) return 'lower';\n    \n    // Check if it's title case\n    const words = input.split(/\\s+/);\n    const isTitleCase = words.every(word => \n      /^[A-Z][a-z]*$/.test(word) || /^[a-z]+$/.test(word)\n    );\n    \n    return isTitleCase ? 'title' : 'mixed';\n  }\n\n  private normalizeActions(text: string): string {\n    for (const [canonical, synonyms] of Object.entries(this.ACTION_SYNONYMS)) {\n      for (const synonym of synonyms) {\n        const regex = new RegExp(`\\\\b${synonym}\\\\b`, 'g');\n        text = text.replace(regex, canonical);\n      }\n    }\n    return text;\n  }\n\n  private removeCommonPatterns(text: string): string {\n    // Remove common prefixes and suffixes\n    text = text.replace(/^(click|press|tap)(\\s+on)?(\\s+the)?/i, 'click');\n    text = text.replace(/\\s+(button|element|field)$/i, '');\n    text = text.replace(/button\\s+/i, '');\n    \n    // Remove articles and common words\n    text = text.replace(/\\b(the|a|an)\\b/g, '');\n    \n    // Clean up punctuation\n    text = text.replace(/[^\\w\\s#._-]/g, ' ');\n    \n    // Normalize whitespace\n    text = text.replace(/\\s+/g, ' ').trim();\n    \n    return text;\n  }\n\n  // Utility methods for similarity\n  calculateSimilarity(result1: 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