测试结果分析师

# 带统计建模的全面测试结果分析
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):
        self.test_results = pd.read_json(test_results_path)
        self.quality_metrics = {}
        self.risk_assessment = {}

    def analyze_test_coverage(self):
        """全面的测试覆盖率分析，含缺口识别"""
        coverage_stats = {
            'line_coverage': self.test_results['coverage']['lines']['pct'],
            'branch_coverage': self.test_results['coverage']['branches']['pct'],
            'function_coverage': self.test_results['coverage']['functions']['pct'],
            'statement_coverage': self.test_results['coverage']['statements']['pct']
        }

        # 识别覆盖率缺口
        uncovered_files = self.test_results['coverage']['files']
        gap_analysis = []

        for file_path, file_coverage in uncovered_files.items():
            if file_coverage['lines']['pct'] < 80:
                gap_analysis.append({
                    'file': file_path,
                    'coverage': file_coverage['lines']['pct'],
                    'risk_level': self._assess_file_risk(file_path, file_coverage),
                    'priority': self._calculate_coverage_priority(file_path, file_coverage)
                })

        return coverage_stats, gap_analysis

    def analyze_failure_patterns(self):
        """失败模式的统计分析与识别"""
        failures = self.test_results['failures']

        # 按类型分类失败
        failure_categories = {
            'functional': [],
            'performance': [],
            'security': [],
            'integration': []
        }

        for failure in failures:
            category = self._categorize_failure(failure)
            failure_categories[category].append(failure)

        # 失败趋势的统计分析
        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):
        """用机器学习模型预测容易出缺陷的区域"""
        # 准备预测模型的特征
        features = self._extract_code_metrics()
        historical_defects = self._load_historical_defect_data()

        # 训练缺陷预测模型
        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)

        # 生成带置信度的预测结果
        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):
        """全面的发布就绪评估"""
        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()
        }

        # 统计置信度计算
        confidence_level = self._calculate_confidence_level(readiness_criteria)

        # 带理由的 Go/No-Go 建议
        recommendation = self._generate_release_recommendation(
            readiness_criteria, confidence_level
        )

        return readiness_criteria, confidence_level, recommendation

    def generate_quality_insights(self):
        """生成可执行的质量洞察和建议"""
        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):
        """生成管理层摘要，带关键指标和战略洞察"""
        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

# [项目名称] 测试结果分析报告

## 管理层摘要
**整体质量评分**：[综合质量评分及趋势分析]
**发布就绪状态**：[GO/NO-GO，附置信度和理由]
**主要质量风险**：[前 3 个风险，附概率和影响评估]
**建议行动**：[优先级行动，附 ROI 分析]

## 测试覆盖率分析
**代码覆盖率**：[行/分支/函数覆盖率及缺口分析]
**功能覆盖率**：[特性覆盖率及基于风险的优先级排序]
**测试有效性**：[缺陷检出率和测试质量指标]
**覆盖率趋势**：[历史覆盖率趋势和改进跟踪]

## 质量指标与趋势
**通过率趋势**：[测试通过率随时间的变化及统计分析]
**缺陷密度**：[每千行代码的缺陷数及行业基准对比]
**性能指标**：[响应时间趋势和 SLA 达标情况]
**安全合规**：[安全测试结果和漏洞评估]

## 缺陷分析与预测
**失败模式分析**：[根因分析及分类]
**缺陷预测**：[基于 ML 的缺陷易发区域预测]
**质量债务评估**：[技术债务对质量的影响]
**预防策略**：[缺陷预防建议]

## 质量 ROI 分析
**质量投入**：[测试工作量和工具成本分析]
**缺陷预防价值**：[早期发现缺陷节省的成本]
**性能影响**：[质量对用户体验和业务指标的影响]
**改进建议**：[高 ROI 的质量改进机会]

---
**分析员**：[姓名]
**分析日期**：[日期]
**数据置信度**：[统计置信度及方法论说明]
**下次评审**：[计划的后续分析和监控安排]