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systematic-debugging

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Systematic Debugging

系统化调试

Overview

概述

Random fixes waste time and create new bugs. Quick patches mask underlying issues.
Core principle: ALWAYS find root cause before attempting fixes. Symptom fixes are failure.
Violating the letter of this process is violating the spirit of debugging.
随机修复会浪费时间并引入新bug。快速补丁会掩盖潜在问题。
核心原则： 在尝试修复前，务必找到根本原因。仅修复症状等同于失败。
违反此流程的字面要求，就是违背调试的核心精神。

The Iron Law

铁则

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
If you haven't completed Phase 1, you cannot propose fixes.
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
如果尚未完成第一阶段，不得提出修复方案。

When to Use

适用场景

Use for ANY technical issue:
  • Test failures
  • Bugs in production
  • Unexpected behavior
  • Performance problems
  • Build failures
  • Integration issues
Use this ESPECIALLY when:
  • Under time pressure (emergencies make guessing tempting)
  • "Just one quick fix" seems obvious
  • You've already tried multiple fixes
  • Previous fix didn't work
  • You don't fully understand the issue
Don't skip when:
  • Issue seems simple (simple bugs have root causes too)
  • You're in a hurry (rushing guarantees rework)
  • Manager wants it fixed NOW (systematic is faster than thrashing)
适用于任何技术问题：
  • 测试失败
  • 生产环境中的bug
  • 意外行为
  • 性能问题
  • 构建失败
  • 集成问题
尤其在以下场景中使用：
  • 处于时间压力下（紧急情况容易让人想当然猜测）
  • “只需快速修复一下”看起来很明显时
  • 已经尝试过多种修复方案时
  • 之前的修复无效时
  • 并未完全理解问题时
请勿跳过此流程的场景：
  • 问题看似简单（简单bug也有根本原因）
  • 时间紧迫（仓促行事必然导致返工）
  • 经理要求立即修复（系统化方法比盲目尝试更快）

The Four Phases

四个阶段

You MUST complete each phase before proceeding to the next.
必须完成每个阶段后，才能进入下一阶段。

Phase 1: Root Cause Investigation

第一阶段：根本原因调查

BEFORE attempting ANY fix:
  1. Read Error Messages Carefully
    • Don't skip past errors or warnings
    • They often contain the exact solution
    • Read stack traces completely
    • Note line numbers, file paths, error codes
  2. Reproduce Consistently
    • Can you trigger it reliably?
    • What are the exact steps?
    • Does it happen every time?
    • If not reproducible → gather more data, don't guess
  3. Check Recent Changes
    • What changed that could cause this?
    • Git diff, recent commits
    • New dependencies, config changes
    • Environmental differences
  4. Gather Evidence in Multi-Component Systems
    WHEN system has multiple components (CI → build → signing, API → service → database):
    BEFORE proposing fixes, add diagnostic instrumentation:
    For EACH component boundary:
  - Log what data enters component
  - Log what data exits component
  - Verify environment/config propagation
  - Check state at each layer

Run once to gather evidence showing WHERE it breaks
THEN analyze evidence to identify failing component
THEN investigate that specific component
    Example (multi-layer system):
    bash
    # Layer 1: Workflow
echo "=== Secrets available in workflow: ==="
echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"

# Layer 2: Build script
echo "=== Env vars in build script: ==="
env | grep IDENTITY || echo "IDENTITY not in environment"

# Layer 3: Signing script
echo "=== Keychain state: ==="
security list-keychains
security find-identity -v

# Layer 4: Actual signing
codesign --sign "$IDENTITY" --verbose=4 "$APP"
    This reveals: Which layer fails (secrets → workflow ✓, workflow → build ✗)
  5. Trace Data Flow
    WHEN error is deep in call stack:
    See 
    root-cause-tracing.md
    in this directory for the complete backward tracing technique.
    Quick version:
    • Where does bad value originate?
    • What called this with bad value?
    • Keep tracing up until you find the source
    • Fix at source, not at symptom
在尝试任何修复之前：
  1. 仔细阅读错误信息
    • 不要跳过错误或警告
    • 它们通常包含确切的解决方案
    • 完整阅读堆栈跟踪（stack traces）
    • 记录行号、文件路径、错误代码
  2. 稳定复现问题
    • 能否可靠触发问题？
    • 确切步骤是什么？
    • 每次都会发生吗？
    • 如果无法复现 → 收集更多数据，不要猜测
  3. 检查近期变更
    • 哪些变更可能导致了这个问题？
    • Git diff、近期提交记录
    • 新的依赖项、配置变更
    • 环境差异
  4. 在多组件系统中收集证据
    当系统包含多个组件时（CI → 构建 → 签名，API → 服务 → 数据库）：
    在提出修复方案前，添加诊断工具：
    For EACH component boundary:
  - Log what data enters component
  - Log what data exits component
  - Verify environment/config propagation
  - Check state at each layer

Run once to gather evidence showing WHERE it breaks
THEN analyze evidence to identify failing component
THEN investigate that specific component
    示例（多层系统）：
    bash
    # Layer 1: Workflow
echo "=== Secrets available in workflow: ==="
echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"

# Layer 2: Build script
echo "=== Env vars in build script: ==="
env | grep IDENTITY || echo "IDENTITY not in environment"

# Layer 3: Signing script
echo "=== Keychain state: ==="
security list-keychains
security find-identity -v

# Layer 4: Actual signing
codesign --sign "$IDENTITY" --verbose=4 "$APP"
    这会揭示： 哪一层出现了故障（密钥 → 工作流 ✓，工作流 → 构建 ✗）
  5. 追踪数据流
    当错误位于调用栈深处时：
    请查看此目录中的
    root-cause-tracing.md
    ，获取完整的反向追踪技术。
    简化版：
    • 错误值源自何处？
    • 是谁传入了错误值？
    • 持续向上追踪，直到找到源头
    • 在源头修复，而非仅修复症状

Phase 2: Pattern Analysis

第二阶段：模式分析

Find the pattern before fixing:
  1. Find Working Examples
    • Locate similar working code in same codebase
    • What works that's similar to what's broken?
  2. Compare Against References
    • If implementing pattern, read reference implementation COMPLETELY
    • Don't skim - read every line
    • Understand the pattern fully before applying
  3. Identify Differences
    • What's different between working and broken?
    • List every difference, however small
    • Don't assume "that can't matter"
  4. Understand Dependencies
    • What other components does this need?
    • What settings, config, environment?
    • What assumptions does it make?
在修复前找到规律：
  1. 寻找可正常运行的示例
    • 在同一代码库中定位类似的可正常运行代码
    • 哪些与故障代码类似的部分是正常工作的？
  2. 与参考实现对比
    • 如果是实现某种模式，请完整阅读参考实现
    • 不要略读 —— 逐行阅读
    • 在应用前，完全理解该模式
  3. 识别差异
    • 正常运行的代码与故障代码之间有哪些不同？
    • 列出所有差异，无论多小
    • 不要假设“这无关紧要”
  4. 理解依赖关系
    • 该组件还依赖哪些其他组件？
    • 需要哪些设置、配置、环境？
    • 它有哪些隐含假设？

Phase 3: Hypothesis and Testing

第三阶段：假设与测试

Scientific method:
  1. Form Single Hypothesis
    • State clearly: "I think X is the root cause because Y"
    • Write it down
    • Be specific, not vague
  2. Test Minimally
    • Make the SMALLEST possible change to test hypothesis
    • One variable at a time
    • Don't fix multiple things at once
  3. Verify Before Continuing
    • Did it work? Yes → Phase 4
    • Didn't work? Form NEW hypothesis
    • DON'T add more fixes on top
  4. When You Don't Know
    • Say "I don't understand X"
    • Don't pretend to know
    • Ask for help
    • Research more
科学方法：
  1. 形成单一假设
    • 清晰陈述：“我认为X是根本原因，因为Y”
    • 将其记录下来
    • 要具体，不要模糊
  2. 最小化测试
    • 做出尽可能小的变更来验证假设
    • 一次只变更一个变量
    • 不要同时修复多个问题
  3. 验证后再继续
    • 问题解决了吗？是 → 进入第四阶段
    • 未解决 → 形成新的假设
    • 不要在现有基础上添加更多修复
  4. 当你不确定时
    • 直接说“我不理解X”
    • 不要假装知道
    • 寻求帮助
    • 进行更多研究

Phase 4: Implementation

第四阶段：实施修复

Fix the root cause, not the symptom:
  1. Create Failing Test Case
    • Simplest possible reproduction
    • Automated test if possible
    • One-off test script if no framework
    • MUST have before fixing
    • Use the 
      superpowers:test-driven-development
      skill for writing proper failing tests
  2. Implement Single Fix
    • Address the root cause identified
    • ONE change at a time
    • No "while I'm here" improvements
    • No bundled refactoring
  3. Verify Fix
    • Test passes now?
    • No other tests broken?
    • Issue actually resolved?
  4. If Fix Doesn't Work
    • STOP
    • Count: How many fixes have you tried?
    • If < 3: Return to Phase 1, re-analyze with new information
    • If ≥ 3: STOP and question the architecture (step 5 below)
    • DON'T attempt Fix #4 without architectural discussion
  5. If 3+ Fixes Failed: Question Architecture
    Pattern indicating architectural problem:
    • Each fix reveals new shared state/coupling/problem in different place
    • Fixes require "massive refactoring" to implement
    • Each fix creates new symptoms elsewhere
    STOP and question fundamentals:
    • Is this pattern fundamentally sound?
    • Are we "sticking with it through sheer inertia"?
    • Should we refactor architecture vs. continue fixing symptoms?
    Discuss with your human partner before attempting more fixes
    This is NOT a failed hypothesis - this is a wrong architecture.
修复根本原因，而非仅修复症状：
  1. 创建失败的测试用例
    • 尽可能简化的复现方式
    • 如有可能，编写自动化测试
    • 若无测试框架，可编写一次性测试脚本
    • 必须在修复前完成
    • 可使用
      superpowers:test-driven-development
      技能来编写规范的失败测试用例
  2. 实施单一修复
    • 针对已确定的根本原因进行修复
    • 一次只做一项变更
    • 不要顺便进行“顺手改进”
    • 不要捆绑重构操作
  3. 验证修复效果
    • 现在测试通过了吗？
    • 其他测试是否被破坏？
    • 问题是否真正解决？
  4. 如果修复无效
    • 停止操作
    • 统计：已经尝试了多少次修复？
    • 如果 <3 次：返回第一阶段，结合新信息重新分析
    • 如果 ≥3 次：停止并质疑架构（见下文第5步）
    • 未进行架构讨论前，请勿尝试第4次修复
  5. 如果3次及以上修复均失败：质疑架构
    表明存在架构问题的模式：
    • 每次修复都会在不同位置暴露出新的共享状态/耦合/问题
    • 修复需要进行“大规模重构”才能实现
    • 每次修复都会在其他地方引发新的症状
    停止并质疑基础问题：
    • 这种模式从根本上是否合理？
    • 我们是否只是“因惯性而坚持”？
    • 我们应该重构架构，还是继续修复症状？
    在尝试更多修复前，请与人类搭档讨论
    这不是假设错误 —— 而是架构本身存在问题。

Red Flags - STOP and Follow Process

危险信号 - 停止并遵循流程

If you catch yourself thinking:
  • "Quick fix for now, investigate later"
  • "Just try changing X and see if it works"
  • "Add multiple changes, run tests"
  • "Skip the test, I'll manually verify"
  • "It's probably X, let me fix that"
  • "I don't fully understand but this might work"
  • "Pattern says X but I'll adapt it differently"
  • "Here are the main problems: [lists fixes without investigation]"
  • Proposing solutions before tracing data flow
  • "One more fix attempt" (when already tried 2+)
  • Each fix reveals new problem in different place
ALL of these mean: STOP. Return to Phase 1.
If 3+ fixes failed: Question the architecture (see Phase 4.5)
如果你发现自己有以下想法：
  • “先快速修复，之后再调查”
  • “试试修改X，看看是否有效”
  • “添加多个变更，然后运行测试”
  • “跳过测试，我会手动验证”
  • “可能是X的问题，我来修复它”
  • “我不完全理解，但这可能有效”
  • “模式要求X，但我会做不同的调整”
  • “主要问题有这些：[列出修复方案但未做调查]”
  • 在追踪数据流前就提出解决方案
  • “再试一次修复”（已经尝试过2次及以上）
  • 每次修复都会在不同地方暴露出新问题
所有这些都意味着：停止操作。返回第一阶段。
如果3次及以上修复失败： 质疑架构（见第四阶段第5步）

your human partner's Signals You're Doing It Wrong

人类搭档发出的“你操作有误”的信号

Watch for these redirections:
  • "Is that not happening?" - You assumed without verifying
  • "Will it show us...?" - You should have added evidence gathering
  • "Stop guessing" - You're proposing fixes without understanding
  • "Ultrathink this" - Question fundamentals, not just symptoms
  • "We're stuck?" (frustrated) - Your approach isn't working
When you see these: STOP. Return to Phase 1.
注意以下纠正信号：
  • “不是这样的吗？”—— 你未经验证就做出了假设
  • “它能显示我们...吗？”—— 你本应添加证据收集步骤
  • “别猜了”—— 你在未理解问题的情况下就提出修复方案
  • “深入思考这个问题”—— 质疑根本问题，而非仅关注症状
  • “我们卡住了？”（语气沮丧）—— 你的方法无效
当看到这些信号时：停止操作。返回第一阶段。

Common Rationalizations

常见的自我辩解

ExcuseReality
"Issue is simple, don't need process"Simple issues have root causes too. Process is fast for simple bugs.
"Emergency, no time for process"Systematic debugging is FASTER than guess-and-check thrashing.
"Just try this first, then investigate"First fix sets the pattern. Do it right from the start.
"I'll write test after confirming fix works"Untested fixes don't stick. Test first proves it.
"Multiple fixes at once saves time"Can't isolate what worked. Causes new bugs.
"Reference too long, I'll adapt the pattern"Partial understanding guarantees bugs. Read it completely.
"I see the problem, let me fix it"Seeing symptoms ≠ understanding root cause.
"One more fix attempt" (after 2+ failures)3+ failures = architectural problem. Question pattern, don't fix again.
借口现实
“问题很简单，不需要遵循流程”简单问题也有根本原因。此流程处理简单bug时速度很快。
“情况紧急，没时间走流程”系统化调试比盲目尝试的乱撞方式更快。
“先试试这个，之后再调查”第一次修复会定下模式。从一开始就正确操作。
“确认修复有效后再写测试”未经过测试的修复无法持久。先写测试能验证问题。
“同时修复多个问题能节省时间”无法确定哪个变更起作用。会引入新bug。
“参考内容太长，我会调整模式”一知半解必然会导致bug。请完整阅读参考内容。
“我看到问题了，我来修复”看到症状 ≠ 理解根本原因。
“再试一次修复”（已经尝试2次及以上）3次及以上失败 = 架构问题。质疑模式，而非继续修复。

Quick Reference

快速参考

PhaseKey ActivitiesSuccess Criteria
1. Root CauseRead errors, reproduce, check changes, gather evidenceUnderstand WHAT and WHY
2. PatternFind working examples, compareIdentify differences
3. HypothesisForm theory, test minimallyConfirmed or new hypothesis
4. ImplementationCreate test, fix, verifyBug resolved, tests pass
阶段核心活动成功标准
1. 根本原因阅读错误信息、复现问题、检查变更、收集证据理解问题是什么以及为什么会发生
2. 模式分析寻找可运行示例、进行对比识别差异
3. 假设验证形成理论、最小化测试假设得到确认或形成新假设
4. 实施修复创建测试、修复问题、验证效果Bug已解决，测试通过

When Process Reveals "No Root Cause"

当流程显示“无根本原因”时

If systematic investigation reveals issue is truly environmental, timing-dependent, or external:
  1. You've completed the process
  2. Document what you investigated
  3. Implement appropriate handling (retry, timeout, error message)
  4. Add monitoring/logging for future investigation
But: 95% of "no root cause" cases are incomplete investigation.
如果系统化调查发现问题确实是环境相关、时间依赖或外部因素导致的：
  1. 你已完成此流程
  2. 记录所做的调查内容
  3. 实施适当的处理措施（重试、超时、错误提示）
  4. 添加监控/日志，以便未来调查
但注意： 95%的“无根本原因”案例都是因为调查不完整。

Supporting Techniques

配套技术

These techniques are part of systematic debugging and available in this directory:
  • root-cause-tracing.md
     - Trace bugs backward through call stack to find original trigger
  • defense-in-depth.md
     - Add validation at multiple layers after finding root cause
  • condition-based-waiting.md
     - Replace arbitrary timeouts with condition polling
Related skills:
  • superpowers:test-driven-development - For creating failing test case (Phase 4, Step 1)
  • superpowers:verification-before-completion - Verify fix worked before claiming success
这些技术是系统化调试的一部分，可在此目录中找到：
  • root-cause-tracing.md
     - 反向追踪调用栈中的bug，找到最初的触发点
  • defense-in-depth.md
     - 找到根本原因后，在多层添加验证机制
  • condition-based-waiting.md
     - 用条件轮询替换任意超时设置
相关技能：
  • superpowers:test-driven-development - 用于创建失败的测试用例（第四阶段第1步）
  • superpowers:verification-before-completion - 在宣称成功前验证修复有效

Real-World Impact

实际效果

From debugging sessions:
  • Systematic approach: 15-30 minutes to fix
  • Random fixes approach: 2-3 hours of thrashing
  • First-time fix rate: 95% vs 40%
  • New bugs introduced: Near zero vs common
来自调试会话的数据：
  • 系统化方法：15-30分钟修复
  • 随机修复方法：2-3小时的盲目尝试
  • 首次修复成功率：95% vs 40%
  • 引入新bug的情况：几乎为零 vs 常见