# Python的動態(tài)特性帶來的調試挑戰(zhàn)示例
class DynamicBehavior:
    def __init__(self):
        self.attributes = {}
    
    def __getattr__(self, name):
        # 動態(tài)屬性訪問可能隱藏潛在問題
        if name in self.attributes:
            return self.attributes[name]
        else:
            # 運行時才暴露的問題
            raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")

# 這類問題在編譯時無法發(fā)現(xiàn)，只能在運行時調試
obj = DynamicBehavior()
obj.attributes["existing"] = "value"
print(obj.existing)  # 正常
print(obj.nonexistent)  # 運行時錯誤

class DebuggingMindset:
    """調試思維框架"""
    
    def __init__(self):
        self.steps = [
            "問題重現(xiàn)",
            "信息收集", 
            "假設生成",
            "實驗驗證",
            "解決方案"
        ]
    
    def apply_framework(self, problem_description):
        """應用五步調試框架"""
        print("=== 應用五步調試框架 ===")
        
        results = {}
        for step in self.steps:
            print(f"\n步驟: {step}")
            method = getattr(self, f"step_{step.lower().replace(' ', '_')}")
            results[step] = method(problem_description)
        
        return results
    
    def step_問題重現(xiàn)(self, problem):
        """步驟1：確保問題可重現(xiàn)"""
        print("目標：創(chuàng)建可靠的問題重現(xiàn)環(huán)境")
        checklist = [
            "? 確定問題發(fā)生的準確條件",
            "? 創(chuàng)建最小重現(xiàn)用例",
            "? 記錄環(huán)境配置和依賴版本",
            "? 驗證問題的一致性"
        ]
        return checklist
    
    def step_信息收集(self, problem):
        """步驟2：全面收集相關信息"""
        print("目標：收集所有相關數(shù)據(jù)和上下文")
        information_sources = [
            "錯誤堆棧跟蹤",
            "日志文件分析", 
            "系統(tǒng)狀態(tài)快照",
            "用戶操作序列",
            "相關配置信息"
        ]
        return information_sources
    
    def step_假設生成(self, problem):
        """步驟3：基于證據(jù)生成假設"""
        print("目標：提出可能的問題根源假設")
        hypothesis_techniques = [
            "分治法：將問題分解為更小的部分",
            "對比法：與正常工作狀態(tài)對比",
            "時間線分析：識別變化點",
            "依賴分析：檢查外部因素影響"
        ]
        return hypothesis_techniques
    
    def step_實驗驗證(self, problem):
        """步驟4：設計實驗驗證假設"""
        print("目標：通過實驗確認或排除假設")
        validation_methods = [
            "單元測試驗證特定功能",
            "日志注入獲取更多信息",
            "條件斷點檢查特定狀態(tài)",
            "A/B測試對比不同配置"
        ]
        return validation_methods
    
    def step_解決方案(self, problem):
        """步驟5：實施并驗證解決方案"""
        print("目標：實施修復并確保問題解決")
        solution_steps = [
            "實施最小化修復",
            "添加回歸測試",
            "驗證修復效果",
            "文檔記錄問題和解決方案"
        ]
        return solution_steps

# 使用示例
debugger = DebuggingMindset()
problem = "應用程序在處理大型數(shù)據(jù)集時內存泄漏"
results = debugger.apply_framework(problem)

class CognitiveBiases:
    """調試中的認知偏見識別"""
    
    def __init__(self):
        self.biases = {
            "確認偏見": "傾向于尋找支持自己假設的證據(jù)",
            "錨定效應": "過分依賴最初獲得的信息",
            "可用性啟發(fā)": "基于容易想到的案例做判斷",
            "專家盲點": "假設其他人擁有與自己相同的知識水平"
        }
    
    def check_biases(self, debugging_process):
        """檢查調試過程中可能存在的認知偏見"""
        warnings = []
        
        if debugging_process.get('first_impression_dominates'):
            warnings.append("?? 錨定效應：可能過分依賴最初的問題判斷")
        
        if debugging_process.get('ignoring_contrary_evidence'):
            warnings.append("?? 確認偏見：可能忽略與當前假設矛盾的證據(jù)")
        
        if debugging_process.get('overconfidence_in_solution'):
            warnings.append("?? 過度自信：可能過早確認問題根源")
        
        return warnings
    
    def mitigation_strategies(self):
        """偏見緩解策略"""
        strategies = {
            "尋求同行評審": "讓他人檢查你的推理過程",
            "系統(tǒng)化記錄": "詳細記錄所有證據(jù)，包括矛盾信息",
            "考慮替代解釋": "主動尋找其他可能的解釋",
            "定期反思": "定期回顧和質疑自己的假設"
        }
        return strategies

# 偏見檢查示例
biases_checker = CognitiveBiases()
debugging_session = {
    'first_impression_dominates': True,
    'ignoring_contrary_evidence': False,
    'overconfidence_in_solution': True
}
warnings = biases_checker.check_biases(debugging_session)
print("認知偏見警告:", warnings)

import logging
import pdb
import traceback
import sys
from functools import wraps
import time

class BasicDebuggingTools:
    """Python基礎調試工具集"""
    
    def __init__(self):
        self.setup_logging()
    
    def setup_logging(self, level=logging.DEBUG):
        """配置日志系統(tǒng)"""
        logging.basicConfig(
            level=level,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler('debug.log'),
                logging.StreamHandler(sys.stdout)
            ]
        )
        self.logger = logging.getLogger(__name__)
    
    def debug_decorator(self, func):
        """調試裝飾器：自動記錄函數(shù)執(zhí)行信息"""
        @wraps(func)
        def wrapper(*args, **kwargs):
            self.logger.debug(f"調用函數(shù): {func.__name__}")
            self.logger.debug(f"參數(shù): args={args}, kwargs={kwargs}")
            
            start_time = time.time()
            try:
                result = func(*args, **kwargs)
                execution_time = time.time() - start_time
                self.logger.debug(f"函數(shù) {func.__name__} 執(zhí)行成功, 耗時: {execution_time:.4f}s")
                return result
            except Exception as e:
                execution_time = time.time() - start_time
                self.logger.error(f"函數(shù) {func.__name__} 執(zhí)行失敗, 耗時: {execution_time:.4f}s")
                self.logger.error(f"錯誤: {str(e)}")
                self.logger.error(traceback.format_exc())
                raise
        
        return wrapper
    
    def interactive_debugging(self, func, *args, **kwargs):
        """交互式調試包裝器"""
        print(f"開始交互式調試函數(shù): {func.__name__}")
        print("設置斷點...")
        
        # 設置跟蹤函數(shù)以進入調試器
        def trace_calls(frame, event, arg):
            if event == 'call':
                filename = frame.f_code.co_filename
                lineno = frame.f_lineno
                print(f"調用: {filename}:{lineno}")
            return trace_calls
        
        # 執(zhí)行函數(shù)
        try:
            sys.settrace(trace_calls)
            result = func(*args, **kwargs)
            sys.settrace(None)
            return result
        except Exception as e:
            print(f"捕獲到異常，進入調試器...")
            traceback.print_exc()
            pdb.post_mortem(sys.exc_info()[2])
    
    def memory_debugging(self, obj):
        """內存使用調試"""
        import sys
        size = sys.getsizeof(obj)
        self.logger.info(f"對象 {type(obj)} 內存大小: {size} 字節(jié)")
        
        if hasattr(obj, '__dict__'):
            for attr, value in obj.__dict__.items():
                attr_size = sys.getsizeof(value)
                self.logger.info(f"  屬性 {attr}: {attr_size} 字節(jié)")
        
        return size

# 使用示例
tools = BasicDebuggingTools()

@tools.debug_decorator
def example_function(data):
    """示例函數(shù)演示調試工具"""
    if not data:
        raise ValueError("數(shù)據(jù)不能為空")
    return [x * 2 for x in data if x > 0]

# 測試調試工具
try:
    result = example_function([1, 2, 3, -1, 0, 4])
    print("結果:", result)
    
    # 測試錯誤情況
    example_function([])
except Exception as e:
    print("捕獲到預期錯誤")

import inspect
import gc
import objgraph
from contextlib import contextmanager
import threading
import cProfile
import pstats
import io

class AdvancedDebuggingTechniques:
    """高級調試技術"""
    
    def __init__(self):
        self.profiler = cProfile.Profile()
    
    @contextmanager
    def performance_profile(self, sort_by='cumulative', limit=10):
        """性能分析上下文管理器"""
        self.profiler.enable()
        try:
            yield
        finally:
            self.profiler.disable()
            
            # 生成分析報告
            s = io.StringIO()
            ps = pstats.Stats(self.profiler, stream=s).sort_stats(sort_by)
            ps.print_stats(limit)
            print("性能分析結果:")
            print(s.getvalue())
    
    def trace_object_lifecycle(self, obj, obj_name):
        """跟蹤對象生命周期"""
        print(f"=== 對象 {obj_name} 生命周期跟蹤 ===")
        
        # 獲取對象信息
        print(f"對象ID: {id(obj)}")
        print(f"對象類型: {type(obj)}")
        print(f"引用計數(shù): {sys.getrefcount(obj) - 1}")  # 減去臨時引用
        
        # 檢查對象是否在垃圾回收器中
        if gc.is_tracked(obj):
            print("對象被垃圾回收器跟蹤")
        else:
            print("對象未被垃圾回收器跟蹤")
    
    def detect_memory_leaks(self, snapshot_before, snapshot_after):
        """檢測內存泄漏"""
        print("=== 內存泄漏檢測 ===")
        
        leaked_objects = []
        for obj in snapshot_after:
            if obj not in snapshot_before:
                # 檢查對象是否應該被釋放
                if not self._is_expected_new_object(obj):
                    leaked_objects.append(obj)
        
        print(f"發(fā)現(xiàn) {len(leaked_objects)} 個可能的內存泄漏對象")
        for obj in leaked_objects[:5]:  # 只顯示前5個
            print(f"泄漏對象: {type(obj)} at {id(obj)}")
        
        return leaked_objects
    
    def _is_expected_new_object(self, obj):
        """判斷對象是否是預期的新對象"""
        # 這里可以實現(xiàn)更復雜的邏輯來判斷對象是否應該存在
        expected_types = [type(None), type(...)]  # 示例
        return type(obj) in expected_types
    
    def thread_debugging(self):
        """多線程調試"""
        print("=== 多線程調試 ===")
        
        # 獲取當前所有線程
        for thread in threading.enumerate():
            print(f"線程: {thread.name} (ID: {thread.ident})")
            print(f"  活動: {thread.is_alive()}")
            print(f"  守護線程: {thread.daemon}")
    
    def dependency_analysis(self, obj):
        """依賴關系分析"""
        print("=== 對象依賴關系分析 ===")
        
        # 顯示引用關系
        print("引用此對象的對象:")
        referrers = gc.get_referrers(obj)
        for ref in referrers[:3]:  # 只顯示前3個引用者
            print(f"  {type(ref)} at {id(ref)}")
        
        print("此對象引用的對象:")
        referents = gc.get_referents(obj)
        for ref in referents[:3]:  # 只顯示前3個被引用者
            print(f"  {type(ref)} at {id(ref)}")

# 使用高級調試技術
advanced_tools = AdvancedDebuggingTechniques()

# 性能分析示例
def performance_intensive_operation():
    """性能密集型操作示例"""
    result = []
    for i in range(10000):
        result.append(i ** 2)
    return result

print("性能分析演示:")
with advanced_tools.performance_profile():
    data = performance_intensive_operation()

# 對象生命周期跟蹤示例
sample_object = {"key": "value"}
advanced_tools.trace_object_lifecycle(sample_object, "sample_dict")

# 多線程調試示例
advanced_tools.thread_debugging()

class DivideAndConquerDebugger:
    """分治調試策略"""
    
    def __init__(self, problem_description):
        self.problem = problem_description
        self.components = []
        self.hypotheses = []
    
    def decompose_problem(self):
        """分解復雜問題為組件"""
        print(f"分解問題: {self.problem}")
        
        # 基于問題類型的不同分解策略
        decomposition_strategies = {
            "performance": self._decompose_performance_issue,
            "memory": self._decompose_memory_issue,
            "logic": self._decompose_logic_issue,
            "integration": self._decompose_integration_issue
        }
        
        # 識別問題類型并應用相應策略
        problem_type = self._classify_problem_type()
        strategy = decomposition_strategies.get(problem_type, self._decompose_general)
        
        self.components = strategy()
        return self.components
    
    def _classify_problem_type(self):
        """分類問題類型"""
        problem_lower = self.problem.lower()
        
        if any(word in problem_lower for word in ['slow', 'performance', 'speed']):
            return "performance"
        elif any(word in problem_lower for word in ['memory', 'leak', 'oom']):
            return "memory"
        elif any(word in problem_lower for word in ['logic', 'calculation', 'algorithm']):
            return "logic"
        elif any(word in problem_lower for word in ['integration', 'api', 'network']):
            return "integration"
        else:
            return "general"
    
    def _decompose_performance_issue(self):
        """分解性能問題"""
        components = [
            {"name": "輸入數(shù)據(jù)規(guī)模", "description": "分析輸入數(shù)據(jù)的大小和復雜度"},
            {"name": "算法復雜度", "description": "檢查算法的時間復雜度"},
            {"name": "I/O操作", "description": "分析文件、網絡等I/O操作"},
            {"name": "外部依賴", "description": "檢查數(shù)據(jù)庫查詢、API調用等"},
            {"name": "系統(tǒng)資源", "description": "分析CPU、內存、磁盤使用情況"}
        ]
        return components
    
    def _decompose_memory_issue(self):
        """分解內存問題"""
        components = [
            {"name": "對象生命周期", "description": "分析對象的創(chuàng)建和銷毀"},
            {"name": "引用循環(huán)", "description": "檢查可能的循環(huán)引用"},
            {"name": "緩存策略", "description": "分析緩存使用和清理"},
            {"name": "數(shù)據(jù)結構和算法", "description": "檢查內存使用效率"},
            {"name": "第三方庫", "description": "分析外部庫的內存使用"}
        ]
        return components
    
    def _decompose_logic_issue(self):
        """分解邏輯問題"""
        components = [
            {"name": "邊界條件", "description": "檢查邊界情況和極端輸入"},
            {"name": "狀態(tài)管理", "description": "分析程序狀態(tài)變化"},
            {"name": "數(shù)據(jù)流", "description": "跟蹤數(shù)據(jù)在系統(tǒng)中的流動"},
            {"name": "條件判斷", "description": "檢查所有條件分支"},
            {"name": "錯誤處理", "description": "分析異常處理邏輯"}
        ]
        return components
    
    def _decompose_integration_issue(self):
        """分解集成問題"""
        components = [
            {"name": "接口兼容性", "description": "檢查API版本和數(shù)據(jù)格式"},
            {"name": "網絡通信", "description": "分析網絡延遲和穩(wěn)定性"},
            {"name": "數(shù)據(jù)序列化", "description": "檢查數(shù)據(jù)編碼和解碼"},
            {"name": "認證授權", "description": "分析權限和認證機制"},
            {"name": "超時和重試", "description": "檢查超時設置和重試邏輯"}
        ]
        return components
    
    def _decompose_general(self):
        """通用問題分解"""
        components = [
            {"name": "輸入驗證", "description": "檢查輸入數(shù)據(jù)的有效性"},
            {"name": "處理邏輯", "description": "分析核心業(yè)務邏輯"},
            {"name": "輸出生成", "description": "檢查結果生成過程"},
            {"name": "錯誤處理", "description": "分析異常情況處理"},
            {"name": "環(huán)境配置", "description": "檢查運行環(huán)境和配置"}
        ]
        return components
    
    def generate_hypotheses(self):
        """為每個組件生成假設"""
        print("\n生成問題假設:")
        
        for component in self.components:
            hypothesis = self._create_hypothesis(component)
            self.hypotheses.append({
                "component": component["name"],
                "hypothesis": hypothesis,
                "priority": self._assign_priority(component),
                "test_method": self._suggest_test_method(component)
            })
        
        return self.hypotheses
    
    def _create_hypothesis(self, component):
        """為組件創(chuàng)建問題假設"""
        hypotheses_templates = {
            "輸入數(shù)據(jù)規(guī)模": "問題可能與大數(shù)據(jù)量處理相關",
            "算法復雜度": "算法效率可能是瓶頸",
            "I/O操作": "磁盤或網絡I/O可能過慢",
            "對象生命周期": "可能存在對象未及時釋放",
            "引用循環(huán)": "可能存在循環(huán)引用阻止垃圾回收",
            "邊界條件": "特定邊界情況可能未被正確處理"
        }
        
        return hypotheses_templates.get(
            component["name"], 
            f"{component['name']}可能存在配置或實現(xiàn)問題"
        )
    
    def _assign_priority(self, component):
        """分配測試優(yōu)先級"""
        high_priority_components = ["算法復雜度", "引用循環(huán)", "邊界條件"]
        if component["name"] in high_priority_components:
            return "高"
        else:
            return "中"
    
    def _suggest_test_method(self, component):
        """建議測試方法"""
        test_methods = {
            "輸入數(shù)據(jù)規(guī)模": "使用不同規(guī)模的數(shù)據(jù)進行測試",
            "算法復雜度": "性能分析和時間復雜度計算",
            "I/O操作": "I/O性能監(jiān)控和優(yōu)化",
            "對象生命周期": "內存分析和對象跟蹤",
            "引用循環(huán)": "循環(huán)引用檢測和垃圾回收分析"
        }
        
        return test_methods.get(
            component["name"],
            "單元測試和集成測試"
        )
    
    def execute_debugging_plan(self):
        """執(zhí)行調試計劃"""
        if not self.hypotheses:
            self.decompose_problem()
            self.generate_hypotheses()
        
        print("\n執(zhí)行調試計劃:")
        results = []
        
        # 按優(yōu)先級排序
        sorted_hypotheses = sorted(
            self.hypotheses, 
            key=lambda x: 0 if x["priority"] == "高" else 1
        )
        
        for hypothesis in sorted_hypotheses:
            print(f"\n測試: {hypothesis['component']}")
            print(f"假設: {hypothesis['hypothesis']}")
            print(f"方法: {hypothesis['test_method']}")
            
            # 這里可以實際執(zhí)行測試
            result = self._execute_test(hypothesis)
            results.append({
                "hypothesis": hypothesis,
                "result": result,
                "confirmed": result.get("issue_found", False)
            })
            
            if result.get("issue_found"):
                print("? 發(fā)現(xiàn)問題!")
                break
            else:
                print("? 未發(fā)現(xiàn)問題，繼續(xù)下一個假設")
        
        return results
    
    def _execute_test(self, hypothesis):
        """執(zhí)行具體的測試（模擬）"""
        # 在實際應用中，這里會執(zhí)行真實的測試代碼
        # 這里返回模擬結果
        
        import random
        test_results = {
            "test_performed": hypothesis["test_method"],
            "execution_time": f"{random.uniform(0.1, 2.0):.2f}s",
            "metrics_collected": ["執(zhí)行時間", "內存使用", "錯誤計數(shù)"],
            "issue_found": random.choice([True, False, False])  # 更傾向于未發(fā)現(xiàn)問題
        }
        
        return test_results

# 使用分治調試策略
complex_problem = "應用程序在處理用戶上傳的大型Excel文件時性能急劇下降"
debugger = DivideAndConquerDebugger(complex_problem)

print("=== 分治調試策略演示 ===")
components = debugger.decompose_problem()
print("\n問題組件:")
for comp in components:
    print(f"- {comp['name']}: {comp['description']}")

hypotheses = debugger.generate_hypotheses()
print("\n生成的假設:")
for hyp in hypotheses:
    print(f"- [{hyp['priority']}] {hyp['component']}: {hyp['hypothesis']}")

results = debugger.execute_debugging_plan()

class ScientificDebuggingApproach:
    """科學調試方法"""
    
    def __init__(self, problem_statement):
        self.problem = problem_statement
        self.observations = []
        self.hypotheses = []
        self.experiments = []
        self.conclusions = []
    
    def observe_and_question(self):
        """觀察和提問階段"""
        print("=== 觀察和提問 ===")
        
        observations = [
            "準確描述觀察到的現(xiàn)象",
            "記錄問題發(fā)生的環(huán)境條件", 
            "確定問題是否可重現(xiàn)",
            "收集相關錯誤信息和日志"
        ]
        
        questions = [
            "問題在什么條件下發(fā)生？",
            "問題在什么條件下不發(fā)生？",
            "最近有什么變化？",
            "問題的嚴重程度如何？"
        ]
        
        self.observations = {
            "facts": observations,
            "questions": questions
        }
        
        return self.observations
    
    def form_hypothesis(self):
        """形成假設階段"""
        print("\n=== 形成假設 ===")
        
        hypothesis_framework = """
        基于以下證據(jù)：
        {evidence}
        
        我假設問題是由：
        {root_cause}
        
        引起的，因為：
        {reasoning}
        """
        
        # 生成多個競爭性假設
        competing_hypotheses = [
            {
                "evidence": "性能下降與數(shù)據(jù)規(guī)模相關",
                "root_cause": "算法時間復雜度問題",
                "reasoning": "大數(shù)據(jù)量下算法效率成為瓶頸",
                "testable_prediction": "處理時間應與數(shù)據(jù)規(guī)模成非線性關系"
            },
            {
                "evidence": "內存使用持續(xù)增長",
                "root_cause": "內存泄漏或緩存策略問題", 
                "reasoning": "未釋放的對象積累導致內存壓力",
                "testable_prediction": "內存使用應隨時間線性增長"
            },
            {
                "evidence": "特定輸入導致問題",
                "root_cause": "邊界條件處理不當",
                "reasoning": "某些輸入數(shù)據(jù)觸發(fā)了未處理的邊緣情況",
                "testable_prediction": "問題應在特定輸入模式復現(xiàn)"
            }
        ]
        
        self.hypotheses = competing_hypotheses
        return self.hypotheses
    
    def design_experiments(self):
        """設計實驗階段"""
        print("\n=== 設計實驗 ===")
        
        experiments = []
        for i, hypothesis in enumerate(self.hypotheses):
            experiment = {
                "hypothesis_id": i,
                "purpose": f"驗證假設: {hypothesis['root_cause']}",
                "method": self._design_experiment_method(hypothesis),
                "metrics": self._define_success_metrics(hypothesis),
                "controls": self._establish_controls()
            }
            experiments.append(experiment)
        
        self.experiments = experiments
        return experiments
    
    def _design_experiment_method(self, hypothesis):
        """設計實驗方法"""
        methods = {
            "算法時間復雜度問題": "使用不同規(guī)模輸入測試執(zhí)行時間，分析時間復雜度",
            "內存泄漏或緩存策略問題": "監(jiān)控內存使用，進行對象生命周期分析",
            "邊界條件處理不當": "系統(tǒng)化測試邊界情況和極端輸入"
        }
        
        return methods.get(
            hypothesis["root_cause"],
            "創(chuàng)建最小重現(xiàn)用例并進行對比測試"
        )
    
    def _define_success_metrics(self, hypothesis):
        """定義成功指標"""
        metrics = {
            "算法時間復雜度問題": ["執(zhí)行時間", "CPU使用率", "時間復雜度"],
            "內存泄漏或緩存策略問題": ["內存使用", "對象數(shù)量", "垃圾回收頻率"],
            "邊界條件處理不當": ["錯誤率", "異常類型", "輸入驗證結果"]
        }
        
        return metrics.get(
            hypothesis["root_cause"],
            ["成功率", "錯誤計數(shù)", "性能指標"]
        )
    
    def _establish_controls(self):
        """建立控制組"""
        return [
            "確保測試環(huán)境一致性",
            "使用相同的輸入數(shù)據(jù)",
            "控制外部變量",
            "多次運行取平均值"
        ]
    
    def execute_and_analyze(self):
        """執(zhí)行和分析階段"""
        print("\n=== 執(zhí)行和分析實驗 ===")
        
        results = []
        for experiment in self.experiments:
            print(f"\n執(zhí)行實驗: {experiment['purpose']}")
            
            # 模擬實驗執(zhí)行
            experiment_result = self._simulate_experiment(experiment)
            analysis = self._analyze_results(experiment, experiment_result)
            
            results.append({
                "experiment": experiment,
                "result": experiment_result,
                "analysis": analysis,
                "hypothesis_supported": analysis["conclusion"] == "supported"
            })
        
        return results
    
    def _simulate_experiment(self, experiment):
        """模擬實驗執(zhí)行（在實際應用中替換為真實實驗）"""
        import random
        
        return {
            "execution_time": f"{random.uniform(1.0, 5.0):.2f}s",
            "data_collected": {
                "metrics": experiment["metrics"],
                "values": [random.randint(1, 100) for _ in experiment["metrics"]]
            },
            "observations": ["測試正常完成", "數(shù)據(jù)收集成功"],
            "issues_encountered": []
        }
    
    def _analyze_results(self, experiment, result):
        """分析實驗結果"""
        # 簡化的分析邏輯
        hypothesis_id = experiment["hypothesis_id"]
        hypothesis = self.hypotheses[hypothesis_id]
        
        # 模擬分析結果
        conclusions = ["supported", "refuted", "inconclusive"]
        conclusion = random.choice(conclusions)
        
        analysis = {
            "data_quality": "good",
            "statistical_significance": "high",
            "conclusion": conclusion,
            "confidence_level": "95%",
            "next_steps": self._suggest_next_steps(conclusion, hypothesis)
        }
        
        return analysis
    
    def _suggest_next_steps(self, conclusion, hypothesis):
        """根據(jù)結論建議下一步"""
        if conclusion == "supported":
            return [f"深入調查 {hypothesis['root_cause']}", "設計修復方案"]
        elif conclusion == "refuted":
            return ["排除此假設", "重新評估其他假設"]
        else:
            return ["改進實驗設計", "收集更多數(shù)據(jù)", "考慮其他假設"]
    
    def draw_conclusions(self, experiment_results):
        """得出結論階段"""
        print("\n=== 得出結論 ===")
        
        supported_hypotheses = [
            result for result in experiment_results 
            if result["hypothesis_supported"]
        ]
        
        if supported_hypotheses:
            best_hypothesis = supported_hypotheses[0]
            hypothesis = self.hypotheses[best_hypothesis["experiment"]["hypothesis_id"]]
            
            conclusion = {
                "status": "RESOLVED",
                "root_cause": hypothesis["root_cause"],
                "confidence": "HIGH",
                "evidence": best_hypothesis["analysis"],
                "recommendation": f"修復 {hypothesis['root_cause']} 相關問題"
            }
        else:
            conclusion = {
                "status": "INCONCLUSIVE", 
                "root_cause": "UNKNOWN",
                "confidence": "LOW",
                "evidence": "所有假設均未被證實",
                "recommendation": "重新觀察問題，生成新的假設"
            }
        
        self.conclusions = conclusion
        return conclusion

# 使用科學調試方法
scientific_debugger = ScientificDebuggingApproach(
    "Web應用在高峰時段響應時間顯著增加"
)

print("=== 科學調試方法演示 ===")
observations = scientific_debugger.observe_and_question()
print("觀察結果:", observations)

hypotheses = scientific_debugger.form_hypothesis()
print("生成的假設:", [h["root_cause"] for h in hypotheses])

experiments = scientific_debugger.design_experiments()
results = scientific_debugger.execute_and_analyze()
conclusions = scientific_debugger.draw_conclusions(results)

print("\n最終結論:", conclusions)

import threading
import time
import queue
from concurrent.futures import ThreadPoolExecutor
import random

class ConcurrencyDebugger:
    """并發(fā)問題調試器"""
    
    def __init__(self):
        self.lock = threading.Lock()
        self.debug_info = []
        self.race_conditions_detected = 0
    
    def debug_thread_safety(self, function, *args, **kwargs):
        """調試線程安全性"""
        print("=== 線程安全性調試 ===")
        
        def wrapped_function(*args, **kwargs):
            thread_name = threading.current_thread().name
            start_time = time.time()
            
            # 記錄線程開始執(zhí)行
            self._log_thread_event(thread_name, "START", function.__name__)
            
            try:
                # 使用鎖確保線程安全（在調試時可以注釋掉來測試競爭條件）
                with self.lock:
                    result = function(*args, **kwargs)
                
                execution_time = time.time() - start_time
                self._log_thread_event(thread_name, "COMPLETE", function.__name__, 
                                     execution_time=execution_time)
                return result
                
            except Exception as e:
                execution_time = time.time() - start_time
                self._log_thread_event(thread_name, "ERROR", function.__name__,
                                     error=str(e), execution_time=execution_time)
                raise
        
        return wrapped_function
    
    def _log_thread_event(self, thread_name, event_type, function_name, 
                         error=None, execution_time=None):
        """記錄線程事件"""
        event = {
            "timestamp": time.time(),
            "thread": thread_name,
            "event": event_type,
            "function": function_name,
            "error": error,
            "execution_time": execution_time
        }
        
        with self.lock:
            self.debug_info.append(event)
    
    def detect_race_conditions(self, shared_resource):
        """檢測競爭條件"""
        print("=== 競爭條件檢測 ===")
        
        original_value = shared_resource.get("counter", 0)
        
        def competing_operation():
            # 非原子操作，可能產生競爭條件
            current = shared_resource.get("counter", 0)
            time.sleep(0.001)  # 增加競爭條件發(fā)生概率
            shared_resource["counter"] = current + 1
        
        # 創(chuàng)建多個線程同時執(zhí)行競爭操作
        threads = []
        for i in range(10):
            thread = threading.Thread(target=competing_operation, name=f"Thread-{i}")
            threads.append(thread)
        
        # 啟動所有線程
        for thread in threads:
            thread.start()
        
        # 等待所有線程完成
        for thread in threads:
            thread.join()
        
        final_value = shared_resource.get("counter", 0)
        expected_value = original_value + 10
        
        if final_value != expected_value:
            self.race_conditions_detected += 1
            print(f"?? 檢測到競爭條件! 期望: {expected_value}, 實際: {final_value}")
            return True
        else:
            print(f"? 未檢測到競爭條件")
            return False
    
    def analyze_deadlocks(self):
        """分析死鎖可能性"""
        print("=== 死鎖分析 ===")
        
        # 檢查線程狀態(tài)
        for thread in threading.enumerate():
            print(f"線程 {thread.name}: {thread.is_alive()}")
        
        # 簡單的死鎖檢測邏輯
        active_threads = [t for t in threading.enumerate() if t.is_alive()]
        if len(active_threads) > 1:
            print(f"有 {len(active_threads)} 個活動線程，可能存在死鎖風險")
        else:
            print("線程狀態(tài)正常")
    
    def stress_test_concurrency(self, function, num_threads=50, num_iterations=100):
        """并發(fā)壓力測試"""
        print(f"=== 并發(fā)壓力測試: {num_threads}線程, {num_iterations}迭代 ===")
        
        results = queue.Queue()
        errors = queue.Queue()
        
        def worker(worker_id):
            for i in range(num_iterations):
                try:
                    result = function(worker_id, i)
                    results.put((worker_id, i, result))
                except Exception as e:
                    errors.put((worker_id, i, str(e)))
        
        # 創(chuàng)建線程池
        with ThreadPoolExecutor(max_workers=num_threads) as executor:
            for i in range(num_threads):
                executor.submit(worker, i)
        
        # 收集結果
        successful_operations = results.qsize()
        failed_operations = errors.qsize()
        
        print(f"成功操作: {successful_operations}")
        print(f"失敗操作: {failed_operations}")
        
        if failed_operations > 0:
            print("在壓力測試中發(fā)現(xiàn)并發(fā)問題!")
            while not errors.empty():
                worker_id, iteration, error = errors.get()
                print(f"錯誤 - 工作線程 {worker_id}, 迭代 {iteration}: {error}")
        
        return {
            "successful": successful_operations,
            "failed": failed_operations,
            "total": successful_operations + failed_operations
        }

# 并發(fā)調試示例
concurrency_debugger = ConcurrencyDebugger()

# 測試共享資源
shared_data = {"counter": 0}

@concurrency_debugger.debug_thread_safety
def thread_safe_increment(counter_dict):
    """線程安全的計數(shù)器遞增"""
    current = counter_dict.get("counter", 0)
    # 模擬一些處理時間
    time.sleep(0.01)
    counter_dict["counter"] = current + 1
    return counter_dict["counter"]

# 測試競爭條件檢測
print("第一次競爭條件檢測:")
concurrency_debugger.detect_race_conditions(shared_data)

print("\n第二次競爭條件檢測:")
concurrency_debugger.detect_race_conditions(shared_data)

# 死鎖分析
concurrency_debugger.analyze_deadlocks()

# 壓力測試
stress_test_result = concurrency_debugger.stress_test_concurrency(
    lambda worker_id, iteration: thread_safe_increment(shared_data),
    num_threads=10,
    num_iterations=5
)

print("\n壓力測試結果:", stress_test_result)
print("調試信息記錄數(shù)量:", len(concurrency_debugger.debug_info))

import gc
import tracemalloc
import psutil
import os

class MemoryDebugger:
    """內存問題調試器"""
    
    def __init__(self):
        self.snapshots = []
        self.memory_history = []
        tracemalloc.start()
    
    def take_memory_snapshot(self, label):
        """獲取內存快照"""
        snapshot = tracemalloc.take_snapshot()
        snapshot_info = {
            "label": label,
            "snapshot": snapshot,
            "timestamp": time.time(),
            "process_memory": self._get_process_memory()
        }
        
        self.snapshots.append(snapshot_info)
        return snapshot_info
    
    def _get_process_memory(self):
        """獲取進程內存使用"""
        process = psutil.Process(os.getpid())
        return process.memory_info().rss / 1024 / 1024  # MB
    
    def compare_snapshots(self, snapshot1_label, snapshot2_label):
        """比較兩個內存快照"""
        snapshot1 = self._find_snapshot(snapshot1_label)
        snapshot2 = self._find_snapshot(snapshot2_label)
        
        if not snapshot1 or not snapshot2:
            print("未找到指定的快照")
            return None
        
        stats = snapshot2["snapshot"].compare_to(snapshot1["snapshot"], 'lineno')
        
        print(f"=== 內存變化分析: {snapshot1_label} -> {snapshot2_label} ===")
        print(f"進程內存變化: {snapshot1['process_memory']:.2f}MB -> {snapshot2['process_memory']:.2f}MB")
        
        # 顯示內存分配最多的10個位置
        print("\n內存分配最多的位置:")
        for stat in stats[:10]:
            print(f"{stat.traceback}: {stat.size / 1024:.2f} KB")
        
        return stats
    
    def _find_snapshot(self, label):
        """根據(jù)標簽查找快照"""
        for snapshot in self.snapshots:
            if snapshot["label"] == label:
                return snapshot
        return None
    
    def detect_memory_leaks(self, function, *args, **kwargs):
        """檢測內存泄漏"""
        print("=== 內存泄漏檢測 ===")
        
        # 強制垃圾回收
        gc.collect()
        
        # 初始快照
        self.take_memory_snapshot("before_function")
        
        # 多次執(zhí)行函數(shù)
        for i in range(5):
            result = function(*args, **kwargs)
            gc.collect()  # 每次執(zhí)行后強制GC
            
            # 記錄內存使用
            current_memory = self._get_process_memory()
            self.memory_history.append({
                "iteration": i,
                "memory_mb": current_memory,
                "timestamp": time.time()
            })
        
        # 最終快照
        self.take_memory_snapshot("after_function")
        
        # 分析內存增長
        self._analyze_memory_growth()
        
        return result
    
    def _analyze_memory_growth(self):
        """分析內存增長模式"""
        if len(self.memory_history) < 2:
            return
        
        initial_memory = self.memory_history[0]["memory_mb"]
        final_memory = self.memory_history[-1]["memory_mb"]
        memory_increase = final_memory - initial_memory
        
        print(f"內存增長分析:")
        print(f"初始內存: {initial_memory:.2f} MB")
        print(f"最終內存: {final_memory:.2f} MB") 
        print(f"內存增長: {memory_increase:.2f} MB")
        
        if memory_increase > 10:  # 10MB閾值
            print("?? 檢測到顯著內存泄漏!")
        elif memory_increase > 1:
            print("?? 檢測到輕微內存增長")
        else:
            print("? 內存使用穩(wěn)定")
    
    def analyze_object_references(self, target_object):
        """分析對象引用關系"""
        print("=== 對象引用分析 ===")
        
        referrers = gc.get_referrers(target_object)
        print(f"對象 {type(target_object)} 被 {len(referrers)} 個對象引用")
        
        for i, referrer in enumerate(referrers[:5]):  # 只顯示前5個
            print(f"引用者 {i+1}: {type(referrer)}")
        
        referents = gc.get_referents(target_object)
        print(f"對象引用 {len(referents)} 個其他對象")
        
        return {
            "referrers_count": len(referrers),
            "referents_count": len(referents)
        }
    
    def monitor_memory_usage(self, duration=30, interval=1):
        """監(jiān)控內存使用情況"""
        print(f"=== 內存使用監(jiān)控 ({duration}秒) ===")
        
        start_time = time.time()
        monitoring_data = []
        
        try:
            while time.time() - start_time < duration:
                memory_info = self._get_memory_details()
                monitoring_data.append(memory_info)
                
                print(f"時間: {time.time() - start_time:.1f}s | "
                      f"內存: {memory_info['process_memory']:.2f}MB | "
                      f"Python對象: {memory_info['object_count']}")
                
                time.sleep(interval)
                
        except KeyboardInterrupt:
            print("監(jiān)控被用戶中斷")
        
        self._generate_memory_report(monitoring_data)
        return monitoring_data
    
    def _get_memory_details(self):
        """獲取詳細內存信息"""
        process_memory = self._get_process_memory()
        
        # 統(tǒng)計Python對象數(shù)量
        object_count = len(gc.get_objects())
        
        return {
            "timestamp": time.time(),
            "process_memory": process_memory,
            "object_count": object_count
        }
    
    def _generate_memory_report(self, monitoring_data):
        """生成內存監(jiān)控報告"""
        if not monitoring_data:
            return
        
        memory_values = [data["process_memory"] for data in monitoring_data]
        max_memory = max(memory_values)
        min_memory = min(memory_values)
        avg_memory = sum(memory_values) / len(memory_values)
        
        print(f"\n內存監(jiān)控報告:")
        print(f"最大內存使用: {max_memory:.2f} MB")
        print(f"最小內存使用: {min_memory:.2f} MB") 
        print(f"平均內存使用: {avg_memory:.2f} MB")
        print(f"內存波動范圍: {max_memory - min_memory:.2f} MB")

# 內存調試示例
memory_debugger = MemoryDebugger()

# 模擬內存密集型函數(shù)
def memory_intensive_operation():
    """模擬內存密集型操作"""
    data = []
    for i in range(1000):
        data.append([j for j in range(1000)])  # 創(chuàng)建大量列表
    return data

# 內存泄漏檢測
print("執(zhí)行內存泄漏檢測:")
result = memory_debugger.detect_memory_leaks(memory_intensive_operation)

# 比較內存快照
memory_debugger.compare_snapshots("before_function", "after_function")

# 對象引用分析
sample_object = {"test": "data", "nested": {"key": "value"}}
memory_debugger.analyze_object_references(sample_object)

# 內存監(jiān)控（縮短時間為5秒用于演示）
print("\n開始內存監(jiān)控:")
monitoring_data = memory_debugger.monitor_memory_usage(duration=5, interval=1)

class ComprehensiveDebuggingWorkflow:
    """完整調試工作流"""
    
    def __init__(self, problem_description):
        self.problem = problem_description
        self.debug_log = []
        self.solutions = []
        
        # 初始化各種調試工具
        self.basic_tools = BasicDebuggingTools()
        self.advanced_tools = AdvancedDebuggingTechniques()
        self.memory_debugger = MemoryDebugger()
        self.concurrency_debugger = ConcurrencyDebugger()
    
    def execute_complete_workflow(self):
        """執(zhí)行完整調試工作流"""
        workflow_steps = [
            self.step_initial_assessment,
            self.step_environment_analysis,
            self.step_reproduce_issue,
            self.step_data_collection,
            self.step_root_cause_analysis,
            self.step_solution_development,
            self.step_verification,
            self.step_documentation
        ]
        
        print("=== 開始完整調試工作流 ===")
        print(f"問題: {self.problem}\n")
        
        results = {}
        for step in workflow_steps:
            step_name = step.__name__.replace('step_', '').replace('_', ' ').title()
            print(f"執(zhí)行步驟: {step_name}")
            
            try:
                step_result = step()
                results[step_name] = step_result
                self.debug_log.append({
                    "step": step_name,
                    "result": step_result,
                    "timestamp": time.time()
                })
                print(f"? {step_name} 完成\n")
                
            except Exception as e:
                error_msg = f"{step_name} 失敗: {str(e)}"
                print(f"? {error_msg}")
                self.debug_log.append({
                    "step": step_name,
                    "error": error_msg,
                    "timestamp": time.time()
                })
        
        return results
    
    def step_initial_assessment(self):
        """步驟1：初步評估"""
        assessment = {
            "problem_urgency": self._assess_urgency(),
            "impact_analysis": self._analyze_impact(),
            "resource_requirements": self._estimate_resources(),
            "stakeholders": self._identify_stakeholders()
        }
        return assessment
    
    def step_environment_analysis(self):
        """步驟2：環(huán)境分析"""
        environment_info = {
            "python_version": sys.version,
            "platform": sys.platform,
            "dependencies": self._check_dependencies(),
            "system_resources": self._check_system_resources()
        }
        return environment_info
    
    def step_reproduce_issue(self):
        """步驟3：重現(xiàn)問題"""
        reproduction_info = {
            "reproducibility": "確定問題重現(xiàn)條件",
            "minimal_test_case": "創(chuàng)建最小重現(xiàn)用例",
            "consistency": "驗證問題一致性",
            "environment_notes": "記錄重現(xiàn)環(huán)境"
        }
        return reproduction_info
    
    def step_data_collection(self):
        """步驟4：數(shù)據(jù)收集"""
        collected_data = {
            "logs": "收集應用日志和系統(tǒng)日志",
            "metrics": "收集性能指標和監(jiān)控數(shù)據(jù)",
            "errors": "收集錯誤信息和堆棧跟蹤",
            "user_reports": "整理用戶報告和反饋"
        }
        return collected_data
    
    def step_root_cause_analysis(self):
        """步驟5：根本原因分析"""
        analysis_techniques = [
            "使用分治法分解問題",
            "應用科學方法生成和驗證假設",
            "使用內存分析工具檢查資源使用",
            "使用性能分析工具識別瓶頸"
        ]
        
        # 模擬根本原因發(fā)現(xiàn)
        potential_causes = [
            "數(shù)據(jù)庫查詢性能問題",
            "內存泄漏導致資源耗盡", 
            "算法效率問題",
            "第三方庫兼容性問題"
        ]
        
        return {
            "techniques_used": analysis_techniques,
            "potential_causes": potential_causes,
            "most_likely_cause": "內存泄漏導致資源耗盡",
            "confidence_level": "高"
        }
    
    def step_solution_development(self):
        """步驟6：解決方案開發(fā)"""
        solution_plan = {
            "immediate_fix": "重啟服務釋放內存",
            "short_term_solution": "優(yōu)化內存使用，添加監(jiān)控",
            "long_term_solution": "重構代碼解決根本問題",
            "rollback_plan": "準備回滾方案"
        }
        
        self.solutions.append(solution_plan)
        return solution_plan
    
    def step_verification(self):
        """步驟7：驗證解決方案"""
        verification_results = {
            "testing_performed": [
                "單元測試通過",
                "集成測試通過", 
                "性能測試達標",
                "用戶驗收測試通過"
            ],
            "metrics_improvement": {
                "memory_usage": "減少40%",
                "response_time": "提升30%",
                "error_rate": "降低至0.1%"
            },
            "regression_check": "無回歸問題發(fā)現(xiàn)"
        }
        return verification_results
    
    def step_documentation(self):
        """步驟8：文檔記錄"""
        documentation = {
            "problem_summary": self.problem,
            "root_cause": "內存泄漏在數(shù)據(jù)處理模塊",
            "solution_applied": "優(yōu)化數(shù)據(jù)結構和緩存策略",
            "lessons_learned": [
                "需要更好的內存監(jiān)控",
                "應該定期進行代碼審查",
                "加強測試覆蓋邊界情況"
            ],
            "prevention_measures": [
                "添加內存使用警報",
                "建立代碼質量門禁",
                "定期進行性能測試"
            ]
        }
        return documentation
    
    def _assess_urgency(self):
        """評估問題緊急程度"""
        urgency_indicators = {
            "user_impact": "高 - 影響所有用戶",
            "business_impact": "中 - 影響核心功能",
            "system_stability": "低 - 系統(tǒng)仍可運行"
        }
        return "中"  # 綜合評估
    
    def _analyze_impact(self):
        """分析問題影響范圍"""
        return {
            "affected_users": "所有用戶",
            "affected_features": "數(shù)據(jù)處理功能",
            "system_components": ["API服務", "數(shù)據(jù)庫", "緩存系統(tǒng)"]
        }
    
    def _estimate_resources(self):
        """估算所需資源"""
        return {
            "time_estimate": "4-8小時",
            "team_size": "2人",
            "tools_required": ["調試器", "性能分析器", "內存分析器"]
        }
    
    def _identify_stakeholders(self):
        """識別相關方"""
        return ["開發(fā)團隊", "產品經理", "運維團隊", "最終用戶"]
    
    def _check_dependencies(self):
        """檢查依賴關系"""
        try:
            import django
            import requests
            import numpy
            return {
                "django": django.__version__,
                "requests": requests.__version__,
                "numpy": numpy.__version__
            }
        except ImportError as e:
            return f"依賴檢查失敗: {str(e)}"
    
    def _check_system_resources(self):
        """檢查系統(tǒng)資源"""
        try:
            import psutil
            return {
                "cpu_usage": f"{psutil.cpu_percent()}%",
                "memory_usage": f"{psutil.virtual_memory().percent}%",
                "disk_usage": f"{psutil.disk_usage('/').percent}%"
            }
        except ImportError:
            return "需要psutil庫來檢查系統(tǒng)資源"
    
    def generate_final_report(self):
        """生成最終調試報告"""
        report = {
            "problem_description": self.problem,
            "debugging_timeline": self.debug_log,
            "solutions_proposed": self.solutions,
            "key_findings": self._extract_key_findings(),
            "recommendations": self._generate_recommendations()
        }
        
        print("\n" + "="*60)
        print("調試完成報告")
        print("="*60)
        
        for key, value in report.items():
            print(f"\n{key.replace('_', ' ').title()}:")
            if isinstance(value, list):
                for item in value:
                    print(f"  - {item}")
            elif isinstance(value, dict):
                for k, v in value.items():
                    print(f"  {k}: {v}")
            else:
                print(f"  {value}")
        
        return report
    
    def _extract_key_findings(self):
        """提取關鍵發(fā)現(xiàn)"""
        return [
            "問題根本原因是數(shù)據(jù)處理模塊的內存泄漏",
            "在高峰使用時段內存使用超出系統(tǒng)限制",
            "解決方案包括優(yōu)化數(shù)據(jù)結構和添加內存監(jiān)控"
        ]
    
    def _generate_recommendations(self):
        """生成改進建議"""
        return [
            "實施持續(xù)的內存使用監(jiān)控",
            "建立性能測試基準",
            "定期進行代碼審查重點關注資源管理",
            "加強開發(fā)團隊的調試技能培訓"
        ]

# 執(zhí)行完整調試工作流示例
print("=== 完整調試工作流演示 ===")

complex_problem = """
Web應用在每日高峰時段（上午10-11點）出現(xiàn)性能嚴重下降，
API響應時間從平均200ms增加到超過5秒，同時系統(tǒng)內存使用持續(xù)增長直至崩潰。
"""

workflow = ComprehensiveDebuggingWorkflow(complex_problem)
results = workflow.execute_complete_workflow()

final_report = workflow.generate_final_report()

class DebuggingMindsetTrainer:
    """調試思維訓練器"""
    
    def __init__(self):
        self.skills = {
            "analytical_thinking": 0,
            "patience": 0,
            "attention_to_detail": 0,
            "systematic_approach": 0,
            "technical_knowledge": 0
        }
        self.practice_sessions = []
    
    def daily_practice_routine(self):
        """日常練習計劃"""
        routine = [
            self.practice_code_review,
            self.practice_problem_decomposition,
            self.practice_hypothesis_generation,
            self.practice_tool_usage,
            self.practice_documentation
        ]
        
        print("=== 調試思維日常練習 ===")
        for practice in routine:
            practice()
        
        self._update_skill_levels()
        self._generate_progress_report()
    
    def practice_code_review(self):
        """練習代碼審查"""
        print("\n1. 代碼審查練習:")
        print("選擇開源項目的一個模塊，嘗試找出潛在問題")
        print("重點觀察: 錯誤處理、資源管理、邊界條件")
        
        review_findings = [
            "檢查異常處理是否完整",
            "驗證資源是否正確釋放",
            "分析算法復雜度",
            "檢查輸入驗證邏輯"
        ]
        
        self._record_practice_session("code_review", review_findings)
    
    def practice_problem_decomposition(self):
        """練習問題分解"""
        print("\n2. 問題分解練習:")
        print("選擇一個復雜問題，分解為可管理的小問題")
        
        sample_problem = "用戶報告應用在特定操作后變得非常緩慢"
        decomposition = [
            "識別慢速操作的具體步驟",
            "分析操作涉及的數(shù)據(jù)規(guī)模",
            "檢查外部依賴性能",
            "評估系統(tǒng)資源使用情況"
        ]
        
        print(f"問題: {sample_problem}")
        print("分解結果:", decomposition)
        self._record_practice_session("problem_decomposition", decomposition)
    
    def practice_hypothesis_generation(self):
        """練習假設生成"""
        print("\n3. 假設生成練習:")
        print("基于有限信息生成多個競爭性假設")
        
        scenario = "數(shù)據(jù)庫查詢在高峰時段變慢"
        hypotheses = [
            "假設1: 數(shù)據(jù)庫連接池耗盡",
            "假設2: 缺少關鍵索引",
            "假設3: 網絡帶寬限制",
            "假設4: 硬件資源不足"
        ]
        
        print(f"場景: {scenario}")
        print("生成的假設:", hypotheses)
        self._record_practice_session("hypothesis_generation", hypotheses)
    
    def practice_tool_usage(self):
        """練習工具使用"""
        print("\n4. 調試工具練習:")
        tools_to_practice = [
            "pdb交互式調試",
            "logging配置和使用",
            "性能分析器",
            "內存分析工具"
        ]
        
        print("本周重點練習工具:", tools_to_practice)
        self._record_practice_session("tool_usage", tools_to_practice)
    
    def practice_documentation(self):
        """練習文檔記錄"""
        print("\n5. 調試文檔練習:")
        documentation_elements = [
            "問題描述清晰準確",
            "重現(xiàn)步驟詳細完整", 
            "環(huán)境信息全面",
            "解決方案記錄完整",
            "經驗教訓總結"
        ]
        
        print("調試文檔應包含:", documentation_elements)
        self._record_practice_session("documentation", documentation_elements)
    
    def _record_practice_session(self, practice_type, findings):
        """記錄練習會話"""
        session = {
            "type": practice_type,
            "findings": findings,
            "timestamp": time.time(),
            "duration_minutes": 15  # 假設每個練習15分鐘
        }
        self.practice_sessions.append(session)
    
    def _update_skill_levels(self):
        """更新技能等級"""
        # 簡單的技能提升邏輯
        for skill in self.skills:
            self.skills[skill] = min(100, self.skills[skill] + 2)
    
    def _generate_progress_report(self):
        """生成進度報告"""
        print("\n" + "="*40)
        print("練習進度報告")
        print("="*40)
        
        total_sessions = len(self.practice_sessions)
        print(f"總練習會話: {total_sessions}")
        
        print("\n技能水平:")
        for skill, level in self.skills.items():
            print(f"  {skill}: {level}/100")
        
        # 計算綜合調試能力
        overall_score = sum(self.skills.values()) / len(self.skills)
        print(f"\n綜合調試能力: {overall_score:.1f}/100")
        
        if overall_score < 50:
            print("建議: 繼續(xù)堅持日常練習")
        elif overall_score < 80:
            print("良好! 考慮參與真實項目調試")
        else:
            print("優(yōu)秀! 可以指導他人調試技巧")

# 調試思維訓練示例
trainer = DebuggingMindsetTrainer()

# 執(zhí)行一周的練習（演示用只執(zhí)行一次）
print("開始調試思維訓練...")
trainer.daily_practice_routine()

class DebuggingBestPractices:
    """調試最佳實踐"""
    
    def __init__(self):
        self.practices = self._compile_best_practices()
    
    def _compile_best_practices(self):
        """編譯最佳實踐"""
        return {
            "mindset": [
                "保持冷靜和耐心",
                "避免過早下結論", 
                "擁抱不確定性",
                "從錯誤中學習"
            ],
            "methodology": [
                "使用科學方法系統(tǒng)化調試",
                "一次只改變一個變量",
                "詳細記錄所有實驗和結果",
                "從簡單假設開始測試"
            ],
            "tools": [
                "熟練掌握基礎調試工具",
                "根據(jù)問題類型選擇合適的工具",
                "建立個人調試工具包",
                "定期學習新工具和技術"
            ],
            "prevention": [
                "編寫可測試的代碼",
                "實施完整的日志策略",
                "建立監(jiān)控和警報系統(tǒng)",
                "定期進行代碼審查"
            ],
            "collaboration": [
                "有效溝通問題描述",
                "尋求同行評審和幫助",
                "分享調試經驗和學習",
                "建立團隊調試標準"
            ]
        }
    
    def get_practice_checklist(self, debug_phase):
        """獲取特定階段的實踐檢查清單"""
        phase_practices = {
            "preparation": [
                "? 準確定義問題現(xiàn)象",
                "? 確認問題重現(xiàn)性", 
                "? 收集環(huán)境信息",
                "? 設定調試目標"
            ],
            "investigation": [
                "? 使用分治法分解問題",
                "? 生成多個競爭性假設",
                "? 設計控制實驗",
                "? 系統(tǒng)化收集數(shù)據(jù)"
            ],
            "resolution": [
                "? 實施最小化修復",
                "? 驗證修復效果",
                "? 檢查回歸問題",
                "? 更新相關文檔"
            ],
            "learning": [
                "? 總結根本原因",
                "? 記錄經驗教訓", 
                "? 分享解決方案",
                "? 更新預防措施"
            ]
        }
        
        return phase_practices.get(debug_phase, [])
    
    def evaluate_debugging_session(self, session_data):
        """評估調試會話質量"""
        evaluation_criteria = {
            "problem_definition": "問題描述是否清晰準確",
            "reproducibility": "是否建立了可靠的重現(xiàn)方法",
            "data_collection": "是否收集了足夠的相關數(shù)據(jù)",
            "hypothesis_quality": "假設是否基于證據(jù)和推理",
            "experiment_design": "實驗設計是否科學合理",
            "solution_effectiveness": "解決方案是否有效且完整",
            "documentation": "文檔記錄是否完整清晰"
        }
        
        scores = {}
        for criterion, description in evaluation_criteria.items():
            # 在實際應用中，這里會有更復雜的評分邏輯
            score = self._score_criterion(session_data, criterion)
            scores[criterion] = {
                "score": score,
                "description": description,
                "feedback": self._generate_feedback(criterion, score)
            }
        
        overall_score = sum(item["score"] for item in scores.values()) / len(scores)
        
        return {
            "scores": scores,
            "overall_score": overall_score,
            "improvement_suggestions": self._get_improvement_suggestions(scores)
        }
    
    def _score_criterion(self, session_data, criterion):
        """評分標準（簡化版）"""
        # 在實際應用中，這里會有更復雜的評分邏輯
        import random
        return random.randint(6, 10)  # 模擬評分
    
    def _generate_feedback(self, criterion, score):
        """生成反饋"""
        if score >= 9:
            return "優(yōu)秀表現(xiàn)"
        elif score >= 7:
            return "良好，有改進空間"
        else:
            return "需要重點改進"
    
    def _get_improvement_suggestions(self, scores):
        """獲取改進建議"""
        suggestions = []
        
        for criterion, data in scores.items():
            if data["score"] < 8:
                suggestions.append(f"改進 {criterion}: {data['description']}")
        
        return suggestions

# 最佳實踐應用示例
best_practices = DebuggingBestPractices()

print("=== 調試最佳實踐 ===")
for category, practices in best_practices.practices.items():
    print(f"\n{category.title()}:")
    for practice in practices:
        print(f"  ? {practice}")

print("\n=== 調試準備階段檢查清單 ===")
preparation_checklist = best_practices.get_practice_checklist("preparation")
for item in preparation_checklist:
    print(item)

# 模擬調試會話評估
sample_session = {
    "problem_definition": "明確",
    "data_collected": "完整",
    "experiments_performed": 5
}

evaluation = best_practices.evaluate_debugging_session(sample_session)
print(f"\n調試會話評估得分: {evaluation['overall_score']:.1f}/10")