src/utils/prioritization.py

"""
🎯 Smart Prioritization System
Severity-based sorting with impact analysis and fix effort estimation
"""
from typing import Dict, List, Any
import re


class IssuePrioritizer:
    """Smart issue prioritization with severity, impact, and effort analysis"""
    
    # Severity weights
    SEVERITY_WEIGHTS = {
        "error": 10,
        "critical": 10,
        "warning": 5,
        "info": 1
    }
    
    # Impact categories (security > reliability > performance > style)
    IMPACT_WEIGHTS = {
        "security": 100,
        "reliability": 50,
        "performance": 25,
        "maintainability": 10,
        "style": 5
    }
    
    # Fix effort estimation keywords
    QUICK_FIX_PATTERNS = [
        "missing semicolon", "unused variable", "console.log",
        "trailing whitespace", "missing docstring", "import order"
    ]
    
    MEDIUM_FIX_PATTERNS = [
        "complexity", "duplicate code", "function too long",
        "too many parameters", "nested loop"
    ]
    
    MAJOR_REFACTOR_PATTERNS = [
        "sql injection", "xss vulnerability", "race condition",
        "memory leak", "architecture", "design pattern"
    ]
    
    @staticmethod
    def classify_severity(issue: Dict[str, Any]) -> str:
        """Classify issue severity"""
        severity = issue.get("severity", "").lower()
        message = issue.get("message", "").lower()
        rule_id = issue.get("rule_id", "").lower()
        
        # Security issues are always critical
        if any(word in message or word in rule_id for word in 
               ["security", "vulnerability", "injection", "xss", "csrf"]):
            return "critical"
        
        # Map existing severities
        if severity in ["error", "critical"]:
            return "critical"
        elif severity == "warning":
            return "high"
        elif severity == "info":
            return "low"
        
        return "medium"
    
    @staticmethod
    def classify_impact(issue: Dict[str, Any]) -> tuple[str, int]:
        """Classify issue impact category and score"""
        message = issue.get("message", "").lower()
        rule_id = issue.get("rule_id", "").lower()
        text = f"{message} {rule_id}"
        
        # Security issues
        if any(word in text for word in 
               ["security", "vulnerability", "injection", "xss", "csrf", 
                "hardcoded", "secret", "token", "password", "weak"]):
            return "security", 100
        
        # Reliability issues
        if any(word in text for word in 
               ["null", "undefined", "exception", "error handling", 
                "race condition", "memory leak", "resource leak"]):
            return "reliability", 50
        
        # Performance issues
        if any(word in text for word in 
               ["performance", "slow", "inefficient", "complexity", 
                "nested loop", "blocking", "synchronous"]):
            return "performance", 25
        
        # Maintainability issues
        if any(word in text for word in 
               ["duplicate", "complexity", "maintainability", "readability",
                "naming", "function length", "parameters"]):
            return "maintainability", 10
        
        # Style issues
        return "style", 5
    
    @staticmethod
    def estimate_fix_effort(issue: Dict[str, Any]) -> tuple[str, int]:
        """Estimate effort to fix (quick/medium/major) with time in minutes"""
        message = issue.get("message", "").lower()
        rule_id = issue.get("rule_id", "").lower()
        text = f"{message} {rule_id}"
        
        # Quick fixes (1-5 minutes)
        for pattern in IssuePrioritizer.QUICK_FIX_PATTERNS:
            if pattern in text:
                return "quick-fix", 2
        
        # Major refactors (30+ minutes)
        for pattern in IssuePrioritizer.MAJOR_REFACTOR_PATTERNS:
            if pattern in text:
                return "major-refactor", 45
        
        # Medium fixes (5-15 minutes)
        for pattern in IssuePrioritizer.MEDIUM_FIX_PATTERNS:
            if pattern in text:
                return "medium-fix", 10
        
        # Default to medium
        return "medium-fix", 10
    
    @classmethod
    def calculate_priority_score(cls, issue: Dict[str, Any]) -> int:
        """Calculate overall priority score (higher = more urgent)"""
        severity = cls.classify_severity(issue)
        impact_category, impact_score = cls.classify_impact(issue)
        effort_category, effort_minutes = cls.estimate_fix_effort(issue)
        
        # Base score from severity
        severity_map = {
            "critical": 1000,
            "high": 500,
            "medium": 100,
            "low": 10
        }
        base_score = severity_map.get(severity, 100)
        
        # Add impact score
        total_score = base_score + impact_score
        
        # Boost quick fixes (we want to encourage quick wins)
        if effort_category == "quick-fix":
            total_score += 50
        
        return total_score
    
    @classmethod
    def enrich_issue(cls, issue: Dict[str, Any]) -> Dict[str, Any]:
        """Enrich issue with priority metadata"""
        enriched = issue.copy()
        
        # Add classifications
        enriched["priority_severity"] = cls.classify_severity(issue)
        impact_cat, impact_score = cls.classify_impact(issue)
        enriched["impact_category"] = impact_cat
        enriched["impact_score"] = impact_score
        
        effort_cat, effort_minutes = cls.estimate_fix_effort(issue)
        enriched["fix_effort"] = effort_cat
        enriched["estimated_fix_time_minutes"] = effort_minutes
        
        # Calculate priority score
        enriched["priority_score"] = cls.calculate_priority_score(issue)
        
        return enriched
    
    @classmethod
    def prioritize_issues(cls, issues: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Sort issues by priority (highest first) and enrich with metadata"""
        # Enrich all issues
        enriched_issues = [cls.enrich_issue(issue) for issue in issues]
        
        # Sort by priority score (descending)
        sorted_issues = sorted(
            enriched_issues,
            key=lambda x: x.get("priority_score", 0),
            reverse=True
        )
        
        return sorted_issues
    
    @staticmethod
    def get_statistics(issues: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Generate statistics from prioritized issues"""
        if not issues:
            return {
                "total": 0,
                "by_severity": {},
                "by_impact": {},
                "by_effort": {},
                "total_fix_time_minutes": 0,
                "quick_wins": 0
            }
        
        stats = {
            "total": len(issues),
            "by_severity": {},
            "by_impact": {},
            "by_effort": {},
            "total_fix_time_minutes": 0,
            "quick_wins": 0
        }
        
        for issue in issues:
            # Count by severity
            severity = issue.get("priority_severity", "medium")
            stats["by_severity"][severity] = stats["by_severity"].get(severity, 0) + 1
            
            # Count by impact
            impact = issue.get("impact_category", "style")
            stats["by_impact"][impact] = stats["by_impact"].get(impact, 0) + 1
            
            # Count by effort
            effort = issue.get("fix_effort", "medium-fix")
            stats["by_effort"][effort] = stats["by_effort"].get(effort, 0) + 1
            
            # Sum fix time
            stats["total_fix_time_minutes"] += issue.get("estimated_fix_time_minutes", 0)
            
            # Count quick wins
            if effort == "quick-fix":
                stats["quick_wins"] += 1
        
        # Add human-readable time estimate
        total_minutes = stats["total_fix_time_minutes"]
        if total_minutes < 60:
            stats["estimated_fix_time"] = f"{total_minutes} minutes"
        else:
            hours = total_minutes // 60
            minutes = total_minutes % 60
            stats["estimated_fix_time"] = f"{hours}h {minutes}m"
        
        return stats


def format_priority_report(issues: List[Dict[str, Any]]) -> str:
    """Format prioritized issues into a readable report"""
    if not issues:
        return "✅ No issues found!"
    
    stats = IssuePrioritizer.get_statistics(issues)
    
    report = f"""
# 📊 Issue Priority Report

## 📈 Summary
- **Total Issues**: {stats['total']}
- **Estimated Fix Time**: {stats['estimated_fix_time']}
- **Quick Wins**: {stats['quick_wins']} issues

## 🚨 By Severity
"""
    
    severity_emojis = {
        "critical": "🔴",
        "high": "🟠",
        "medium": "🟡",
        "low": "🔵"
    }
    
    for severity in ["critical", "high", "medium", "low"]:
        count = stats["by_severity"].get(severity, 0)
        if count > 0:
            emoji = severity_emojis.get(severity, "⚪")
            report += f"- {emoji} **{severity.title()}**: {count}\n"
    
    report += "\n## 🎯 By Impact\n"
    impact_emojis = {
        "security": "🛡️",
        "reliability": "⚡",
        "performance": "🚀",
        "maintainability": "🔧",
        "style": "🎨"
    }
    
    for impact in ["security", "reliability", "performance", "maintainability", "style"]:
        count = stats["by_impact"].get(impact, 0)
        if count > 0:
            emoji = impact_emojis.get(impact, "📌")
            report += f"- {emoji} **{impact.title()}**: {count}\n"
    
    report += "\n## ⏱️ By Fix Effort\n"
    effort_labels = {
        "quick-fix": "⚡ Quick Fix (< 5 min)",
        "medium-fix": "🔧 Medium Fix (5-15 min)",
        "major-refactor": "🏗️ Major Refactor (30+ min)"
    }
    
    for effort in ["quick-fix", "medium-fix", "major-refactor"]:
        count = stats["by_effort"].get(effort, 0)
        if count > 0:
            label = effort_labels.get(effort, effort)
            report += f"- {label}: {count}\n"
    
    # Top 5 highest priority issues
    report += "\n## 🔥 Top Priority Issues\n\n"
    
    for i, issue in enumerate(issues[:5], 1):
        severity_emoji = severity_emojis.get(issue.get("priority_severity", "medium"), "⚪")
        impact_emoji = impact_emojis.get(issue.get("impact_category", "style"), "📌")
        
        line = issue.get("line", "?")
        message = issue.get("message", "No description")
        effort = issue.get("fix_effort", "medium-fix")
        fix_time = issue.get("estimated_fix_time_minutes", "?")
        
        report += f"{i}. {severity_emoji} {impact_emoji} **Line {line}**: {message}\n"
        report += f"   ⏱️ ~{fix_time} min to fix\n\n"
    
    return report