src/confidence.ts

export interface ConfidenceFactors {
  patternMatchScore: number
  contextRichness: number
  historicalAccuracy: number
  severityMatchesKnownBug: boolean
  hasExplicitEvidence: boolean
  teamAcceptanceRate: number
  patternTypeAccuracy: number
}

export interface CalibratedConfidence {
  score: number
  factors: ConfidenceFactors
  reasoning: string
}

const KNOWN_BUG_PATTERNS: Array<{
  pattern: RegExp
  severity: 'critical' | 'major' | 'minor' | 'info'
  category: string
}> = [
  {
    pattern: /null.*pointer|NPE|null.*check/i,
    severity: 'critical',
    category: 'null-pointer'
  },
  {
    pattern: /sql.*injection|SELECT.*WHERE.*\+|query.*concat/i,
    severity: 'critical',
    category: 'sql-injection'
  },
  {
    pattern: /password|secret|api.*key.*=.*['"]|token.*=.*['"]/i,
    severity: 'critical',
    category: 'secret-exposure'
  },
  {
    pattern: /eval\(|new.*Function\(|innerHTML\s*=|document\.write/i,
    severity: 'critical',
    category: 'xss'
  },
  {
    pattern: /race.*condition|concurrent.*modif|deadlock|mutex/i,
    severity: 'major',
    category: 'concurrency'
  },
  {
    pattern: /memory.*leak|dispose\(|close\(|gc\./i,
    severity: 'major',
    category: 'resource-leak'
  },
  {
    pattern: /async.*without.*await|unhandled.*promise|catch.*without.*throw/i,
    severity: 'major',
    category: 'async-issues'
  },
  {
    pattern: /try.*without.*catch|catch.*empty|swallow.*exception/i,
    severity: 'minor',
    category: 'error-handling'
  },
  {
    pattern: /any.*type|cast.*any|as.*any/i,
    severity: 'info',
    category: 'type-safety'
  },
  {
    pattern: /console\.(log|debug|warn)/i,
    severity: 'info',
    category: 'logging'
  },
  {
    pattern: /TODO|FIXME|HACK|XXX|BUG:/i,
    severity: 'info',
    category: 'tech-debt'
  },
  {
    pattern: /division.*by.*zero|divide.*0|x\s*\/\s*0/i,
    severity: 'major',
    category: 'arithmetic'
  },
  {
    pattern: /off.*by.*one|index.*length|length.*-\s*1/i,
    severity: 'minor',
    category: 'logic-error'
  },
  {
    pattern: /useEffect.*missing.*dep|dependency.*array/i,
    severity: 'minor',
    category: 'react-hooks'
  },
  {
    pattern: /mutation.*props|props.*change|immutable/i,
    severity: 'minor',
    category: 'immutability'
  },
  {
    pattern: /hardcoded|hard.*coded/i,
    severity: 'minor',
    category: 'hardcoding'
  },
  {
    pattern: /circular.*depend|lazy.*load|circular/i,
    severity: 'major',
    category: 'architecture'
  },
  {
    pattern: /sql.*string|query.*build|string.*concat.*sql/i,
    severity: 'critical',
    category: 'sql-injection'
  },
  {
    pattern: /crypto.*md5|sha1.*encrypt|md5.*hash/i,
    severity: 'critical',
    category: 'crypto'
  },
  {
    pattern: /http.*not.*https|mixed.*content|insecure.*request/i,
    severity: 'major',
    category: 'security'
  },
  {
    pattern: /cookie.*without.*secure|secure.*flag.*false/i,
    severity: 'major',
    category: 'security'
  },
  {
    pattern: /sudo|chmod\s+777|root.*login/i,
    severity: 'critical',
    category: 'security'
  },
  {
    pattern: /catch.*(e|err).*{[\s]*}/i,
    severity: 'minor',
    category: 'error-handling'
  },
  {
    pattern: /setTimeout.*0|setImmediate/i,
    severity: 'info',
    category: 'timing'
  },
  {
    pattern: /double.*click|race.*click|multiple.*submit/i,
    severity: 'minor',
    category: 'ux'
  }
]

const POSITIVE_INDICATORS = [
  {indicator: 'suggestion', weight: 0.15},
  {indicator: '```suggestion', weight: 0.2},
  {indicator: 'fix:', weight: 0.1},
  {indicator: 'fixed code', weight: 0.1},
  {indicator: 'correct', weight: 0.05},
  {indicator: 'instead of', weight: 0.05},
  {indicator: 'should be', weight: 0.05},
  {indicator: 'best practice', weight: 0.05}
]

const NEGATIVE_INDICATORS = [
  {indicator: 'maybe', weight: -0.15},
  {indicator: 'might', weight: -0.1},
  {indicator: 'possibly', weight: -0.1},
  {indicator: 'i think', weight: -0.2},
  {indicator: 'unclear', weight: -0.1},
  {indicator: '不确定', weight: -0.15},
  {indicator: 'perhaps', weight: -0.1},
  {indicator: 'could be', weight: -0.1},
  {indicator: 'not sure', weight: -0.15},
  {indicator: 'likely', weight: -0.05}
]

export class ConfidenceCalibrator {
  private historicalAccuracies: Map<string, number[]> = new Map()
  private feedbackHistory: Array<{
    pattern: string
    helpful: boolean
    timestamp: number
  }> = []
  private readonly persistPath = '/tmp/prix_feedback.json'
  private feedbackTracker: any = null

  constructor(feedbackTracker: any = null) {
    this.loadPersisted()
    if (feedbackTracker) {
      this.feedbackTracker = feedbackTracker
    } else {
      try {
        const {FeedbackTracker} = require('./feedback-tracker')
        this.feedbackTracker = new FeedbackTracker()
      } catch (e) {
        // FeedbackTracker not available, continue without it
      }
    }
  }

  private loadPersisted(): void {
    try {
      if (require('fs').existsSync(this.persistPath)) {
        const data = JSON.parse(
          require('fs').readFileSync(this.persistPath, 'utf8')
        )
        if (Array.isArray(data)) {
          this.feedbackHistory = data.map((item: any) => ({
            pattern: item.pattern || '',
            helpful: item.helpful || false,
            timestamp: item.timestamp || Date.now()
          }))
          this.rebuildHistoricalAccuracies()
        }
      }
    } catch (e) {
      // Start fresh if persistence fails
    }
  }

  private persist(): void {
    try {
      require('fs').writeFileSync(
        this.persistPath,
        JSON.stringify(this.feedbackHistory.slice(-1000))
      )
    } catch (e) {
      // Persistence failure is non-fatal
    }
  }

  private rebuildHistoricalAccuracies(): void {
    const patternMap = new Map<string, number[]>()
    for (const entry of this.feedbackHistory) {
      const accuracies = patternMap.get(entry.pattern) || []
      accuracies.push(entry.helpful ? 1 : 0)
      patternMap.set(entry.pattern, accuracies)
    }
    for (const [pattern, accuracies] of patternMap) {
      this.historicalAccuracies.set(pattern, accuracies)
    }
  }

  recordFeedback(pattern: string, helpful: boolean): void {
    this.feedbackHistory.push({pattern, helpful, timestamp: Date.now()})
    const accuracies = this.historicalAccuracies.get(pattern) || []
    accuracies.push(helpful ? 1 : 0)
    this.historicalAccuracies.set(pattern, accuracies)
    this.persist()
  }

  calibrate(
    aiReportedConfidence: number | undefined,
    comment: string,
    contextTokens: number,
    maxContextTokens: number,
    severity: string | undefined,
    teamId?: string,
    repoSlug?: string,
    patternType?: string,
    filename?: string
  ): CalibratedConfidence {
    const factors = this.calculateFactors(
      comment,
      contextTokens,
      maxContextTokens,
      severity,
      teamId,
      repoSlug,
      patternType,
      filename
    )

    let score: number

    if (aiReportedConfidence !== undefined) {
      const deBiased = this.removeLLMBias(aiReportedConfidence)
      score = this.combineWithFactors(deBiased, factors)
    } else {
      score = this.computeFromFactorsOnly(factors)
    }

    score = Math.max(0, Math.min(100, score))

    return {
      score: Math.round(score),
      factors,
      reasoning: this.generateReasoning(factors, score)
    }
  }

  private calculateFactors(
    comment: string,
    contextTokens: number,
    maxContextTokens: number,
    severity: string | undefined,
    teamId?: string,
    repoSlug?: string,
    patternType?: string,
    filename?: string
  ): ConfidenceFactors {
    const patternMatchScore = this.calculatePatternMatch(comment)
    const contextRichness = Math.min(
      1,
      contextTokens / Math.max(1, maxContextTokens * 0.5)
    )
    const historicalAccuracy = this.calculateHistoricalAccuracy(comment)
    const severityMatchesKnownBug = severity
      ? this.matchesKnownBugPattern(comment, severity)
      : this.matchesAnyKnownBugPattern(comment)
    const hasExplicitEvidence = this.hasExplicitEvidence(comment)
    const {teamAcceptanceRate, patternTypeAccuracy} = this.calculateTeamMetrics(
      teamId,
      repoSlug,
      patternType,
      filename
    )

    return {
      patternMatchScore,
      contextRichness,
      historicalAccuracy,
      severityMatchesKnownBug,
      hasExplicitEvidence,
      teamAcceptanceRate,
      patternTypeAccuracy
    }
  }

  private calculateTeamMetrics(
    teamId?: string,
    repoSlug?: string,
    patternType?: string,
    filename?: string
  ): {teamAcceptanceRate: number; patternTypeAccuracy: number} {
    if (!this.feedbackTracker || !teamId || !repoSlug) {
      return {teamAcceptanceRate: 0.5, patternTypeAccuracy: 0.5}
    }

    let teamAcceptanceRate = 0.5
    let patternTypeAccuracy = 0.5

    if (patternType && filename) {
      teamAcceptanceRate = this.feedbackTracker.getPatternAcceptanceRate(
        patternType,
        teamId,
        repoSlug
      )
      patternTypeAccuracy = this.feedbackTracker.getFilenameAcceptanceRate(
        filename,
        teamId,
        repoSlug
      )
    }

    return {teamAcceptanceRate, patternTypeAccuracy}
  }

  private calculatePatternMatch(comment: string): number {
    const lowerComment = comment.toLowerCase()
    let score = 0.5

    for (const {indicator, weight} of POSITIVE_INDICATORS) {
      if (lowerComment.includes(indicator.toLowerCase())) {
        score += weight
      }
    }

    for (const {indicator, weight} of NEGATIVE_INDICATORS) {
      if (lowerComment.includes(indicator.toLowerCase())) {
        score += weight
      }
    }

    if (lowerComment.includes('why this matters')) {
      score += 0.1
    }

    if (
      lowerComment.includes('production') ||
      lowerComment.includes('production environment')
    ) {
      score += 0.05
    }

    return Math.max(0, Math.min(1, score))
  }

  private calculateHistoricalAccuracy(comment: string): number {
    const lowerComment = comment.toLowerCase()

    for (const [pattern, accuracies] of this.historicalAccuracies) {
      if (lowerComment.includes(pattern.toLowerCase())) {
        const avg = accuracies.reduce((a, b) => a + b, 0) / accuracies.length
        return avg
      }
    }

    const relevantFeedback = this.feedbackHistory.filter(f =>
      f.pattern
        .split(' ')
        .some(
          word => word.length > 4 && lowerComment.includes(word.toLowerCase())
        )
    )

    if (relevantFeedback.length === 0) {
      return 0.5
    }

    const accuracy =
      relevantFeedback.filter(f => f.helpful).length / relevantFeedback.length
    return accuracy
  }

  private matchesKnownBugPattern(comment: string, severity: string): boolean {
    const lowerComment = comment.toLowerCase()

    for (const {pattern, severity: patternSeverity} of KNOWN_BUG_PATTERNS) {
      if (pattern.test(lowerComment) && patternSeverity === severity) {
        return true
      }
    }

    return false
  }

  private matchesAnyKnownBugPattern(comment: string): boolean {
    const lowerComment = comment.toLowerCase()

    for (const {pattern} of KNOWN_BUG_PATTERNS) {
      if (pattern.test(lowerComment)) {
        return true
      }
    }

    return false
  }

  private hasExplicitEvidence(comment: string): boolean {
    const explicitPhrases = [
      'this will crash',
      'this causes',
      'this leads to',
      'results in',
      'fails when',
      'breaks when',
      'error when',
      'crash if',
      'exception if',
      'undefined behavior',
      'undefined is',
      'will throw',
      'will fail'
    ]

    const lowerComment = comment.toLowerCase()
    return explicitPhrases.some(phrase => lowerComment.includes(phrase))
  }

  private removeLLMBias(reportedConfidence: number): number {
    if (reportedConfidence >= 95) {
      return 75
    }
    if (reportedConfidence >= 85) {
      return 70
    }
    if (reportedConfidence >= 70) {
      return 62
    }
    if (reportedConfidence >= 60) {
      return 55
    }
    return reportedConfidence * 0.9
  }

  private combineWithFactors(
    baseScore: number,
    factors: ConfidenceFactors
  ): number {
    const weights = {
      base: 0.25,
      pattern: 0.2,
      context: 0.1,
      historical: 0.15,
      knownBug: 0.05,
      teamAcceptance: 0.15,
      patternAccuracy: 0.1
    }

    let score = baseScore * weights.base

    score += factors.patternMatchScore * 100 * weights.pattern
    score += factors.contextRichness * 100 * weights.context
    score += factors.historicalAccuracy * 100 * weights.historical

    if (factors.severityMatchesKnownBug) {
      score += 10 * weights.knownBug
    }

    if (factors.hasExplicitEvidence) {
      score += 5
    }

    score += factors.teamAcceptanceRate * 100 * weights.teamAcceptance
    score += factors.patternTypeAccuracy * 100 * weights.patternAccuracy

    return score
  }

  private computeFromFactorsOnly(factors: ConfidenceFactors): number {
    const weights = {
      pattern: 0.3,
      context: 0.15,
      historical: 0.2,
      knownBug: 0.15,
      teamAcceptance: 0.12,
      patternAccuracy: 0.08
    }

    let score = 50

    score += factors.patternMatchScore * 50 * weights.pattern
    score += factors.contextRichness * 50 * weights.context
    score += factors.historicalAccuracy * 50 * weights.historical

    if (factors.severityMatchesKnownBug) {
      score += 10 * weights.knownBug
    }

    if (factors.hasExplicitEvidence) {
      score += 5
    }

    score += factors.teamAcceptanceRate * 50 * weights.teamAcceptance
    score += factors.patternTypeAccuracy * 50 * weights.patternAccuracy

    return score
  }

  private generateReasoning(factors: ConfidenceFactors, score: number): string {
    const reasons: string[] = []

    if (factors.patternMatchScore > 0.7) {
      reasons.push('strong pattern match')
    } else if (factors.patternMatchScore < 0.4) {
      reasons.push('weak pattern match')
    }

    if (factors.contextRichness > 0.8) {
      reasons.push('rich context')
    } else if (factors.contextRichness < 0.3) {
      reasons.push('limited context')
    }

    if (factors.historicalAccuracy > 0.7) {
      reasons.push('historically accurate')
    } else if (
      factors.historicalAccuracy < 0.4 &&
      factors.historicalAccuracy !== 0.5
    ) {
      reasons.push('historically unreliable')
    }

    if (factors.severityMatchesKnownBug) {
      reasons.push('matches known bug pattern')
    }

    if (factors.hasExplicitEvidence) {
      reasons.push('has explicit evidence')
    }

    if (factors.teamAcceptanceRate > 0.6) {
      reasons.push('high team acceptance')
    } else if (
      factors.teamAcceptanceRate < 0.3 &&
      factors.teamAcceptanceRate !== 0.5
    ) {
      reasons.push('low team acceptance')
    }

    if (factors.patternTypeAccuracy > 0.6) {
      reasons.push('matches accepted patterns')
    }

    return reasons.length > 0
      ? `Calibrated based on: ${reasons.join(', ')}`
      : 'Calibrated using default heuristics'
  }
}

export const confidenceCalibrator = new ConfidenceCalibrator()