# Comprehensive test result analysis with statistical modeling
import json
import math
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

class TestResultsAnalyzer:
    def __init__(self, test_results_path):
        # Coverage is a nested report object, not a rectangular DataFrame.
        with open(test_results_path, encoding='utf-8') as report:
            self.test_results = json.load(report)
        if not isinstance(self.test_results, dict):
            raise ValueError('Expected one JSON report object')
        self.quality_metrics = {}
        self.risk_assessment = {}
        
    def analyze_test_coverage(self):
        """Comprehensive test coverage analysis with gap identification"""
        coverage = self.test_results.get('coverage')
        if not isinstance(coverage, dict):
            raise ValueError('Missing coverage object; no coverage claim can be made')

        def percentage(section, label):
            value = section.get('pct') if isinstance(section, dict) else None
            if (isinstance(value, bool) or not isinstance(value, (int, float))
                    or not math.isfinite(value) or not 0 <= value <= 100):
                raise ValueError(f'{label}.pct must be a finite percentage in [0, 100]')
            return value

        coverage_stats = {
            f'{name[:-1] if name != "branches" else "branch"}_coverage':
                percentage(coverage.get(name), name)
            for name in ('lines', 'branches', 'functions', 'statements')
        }
        files = coverage.get('files')
        if not isinstance(files, dict):
            raise ValueError('coverage.files must map paths to coverage objects')
        gap_analysis = []
        for file_path, file_coverage in files.items():
            if not isinstance(file_coverage, dict):
                raise ValueError(f'Invalid coverage object for {file_path}')
            line_pct = percentage(file_coverage.get('lines'), file_path)
            if line_pct < 80:
                gap_analysis.append({'file': file_path, 'coverage': line_pct})
        # Coverage gaps identify unexecuted code; attach risk using actual criticality.
        return coverage_stats, gap_analysis

    def analyze_failure_patterns(self):
        """Statistical analysis of test failures and pattern identification"""
        failures = self.test_results['failures']
        
        # Categorize failures by type
        failure_categories = {
            'functional': [],
            'performance': [],
            'security': [],
            'integration': []
        }
        
        for failure in failures:
            category = self._categorize_failure(failure)
            failure_categories[category].append(failure)
        
        # Statistical analysis of failure trends
        failure_trends = self._analyze_failure_trends(failure_categories)
        root_causes = self._identify_root_causes(failures)
        
        return failure_categories, failure_trends, root_causes
    
    def predict_defect_prone_areas(self):
        """Machine learning model for defect prediction"""
        # Prepare features for prediction model
        features = self._extract_code_metrics()
        historical_defects = self._load_historical_defect_data()
        
        # Train defect prediction model
        X_train, X_test, y_train, y_test = train_test_split(
            features, historical_defects, test_size=0.2, random_state=42
        )
        
        model = RandomForestClassifier(n_estimators=100, random_state=42)
        model.fit(X_train, y_train)
        
        # Generate predictions with confidence scores
        predictions = model.predict_proba(features)
        feature_importance = model.feature_importances_
        
        return predictions, feature_importance, model.score(X_test, y_test)
    
    def assess_release_readiness(self):
        """Comprehensive release readiness assessment"""
        readiness_criteria = {
            'test_pass_rate': self._calculate_pass_rate(),
            'coverage_threshold': self._check_coverage_threshold(),
            'performance_sla': self._validate_performance_sla(),
            'security_compliance': self._check_security_compliance(),
            'defect_density': self._calculate_defect_density(),
            'risk_score': self._calculate_overall_risk_score()
        }
        
        # Statistical confidence calculation
        confidence_level = self._calculate_confidence_level(readiness_criteria)
        
        # Go/No-Go recommendation with reasoning
        recommendation = self._generate_release_recommendation(
            readiness_criteria, confidence_level
        )
        
        return readiness_criteria, confidence_level, recommendation
    
    def generate_quality_insights(self):
        """Generate actionable quality insights and recommendations"""
        insights = {
            'quality_trends': self._analyze_quality_trends(),
            'improvement_opportunities': self._identify_improvement_opportunities(),
            'resource_optimization': self._recommend_resource_optimization(),
            'process_improvements': self._suggest_process_improvements(),
            'tool_recommendations': self._evaluate_tool_effectiveness()
        }
        
        return insights
    
    def create_executive_report(self):
        """Generate executive summary with key metrics and strategic insights"""
        report = {
            'overall_quality_score': self._calculate_overall_quality_score(),
            'quality_trend': self._get_quality_trend_direction(),
            'key_risks': self._identify_top_quality_risks(),
            'business_impact': self._assess_business_impact(),
            'investment_recommendations': self._recommend_quality_investments(),
            'success_metrics': self._track_quality_success_metrics()
        }
        
        return report

{"coverage":{"lines":{"pct":90},"branches":{"pct":80},"functions":{"pct":95},"statements":{"pct":90},"files":{"src/payment.py":{"lines":{"pct":60}}}}}

# [Project Name] Test Results Analysis Report

## 📊 Executive Summary
**Overall Quality Score**: [Composite quality score with trend analysis]
**Release Readiness**: [GO/NO-GO with confidence level and reasoning]
**Key Quality Risks**: [Top 3 risks with probability and impact assessment]
**Recommended Actions**: [Priority actions with ROI analysis]

## 🔍 Test Coverage Analysis
**Code Coverage**: [Line/Branch/Function coverage with gap analysis]
**Functional Coverage**: [Feature coverage with risk-based prioritization]
**Test Effectiveness**: [Defect detection rate and test quality metrics]
**Coverage Trends**: [Historical coverage trends and improvement tracking]

## 📈 Quality Metrics and Trends
**Pass Rate Trends**: [Test pass rate over time with statistical analysis]
**Defect Density**: [Defects per KLOC with benchmarking data]
**Performance Metrics**: [Response time trends and SLA compliance]
**Security Compliance**: [Security test results and vulnerability assessment]

## 🎯 Defect Analysis and Predictions
**Failure Pattern Analysis**: [Root cause analysis with categorization]
**Defect Prediction**: [ML-based predictions for defect-prone areas]
**Quality Debt Assessment**: [Technical debt impact on quality]
**Prevention Strategies**: [Recommendations for defect prevention]

## 💰 Quality ROI Analysis
**Quality Investment**: [Testing effort and tool costs analysis]
**Defect Prevention Value**: [Cost savings from early defect detection]
**Performance Impact**: [Quality impact on user experience and business metrics]
**Improvement Recommendations**: [High-ROI quality improvement opportunities]

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**Test Results Analyzer**: [Your name]
**Analysis Date**: [Date]
**Data Confidence**: [Statistical confidence level with methodology]
**Next Review**: [Scheduled follow-up analysis and monitoring]