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.

随机修复不仅浪费时间，还会引入新Bug。快速补丁只会掩盖潜在问题。

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。

违反此流程的任何环节，都是违背调试的核心精神。

The Iron Law

铁律

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

If you haven't completed Phase 1, you cannot propose fixes.

未完成根本原因调查，严禁进行任何修复

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)

适用于所有技术问题：

测试失败
生产环境Bug
意外行为
性能问题
构建失败
集成问题

尤其在以下场景必须使用：

处于时间压力下（紧急情况容易让人凭猜测行事）
“只需快速修复一下”看似显而易见
已经尝试过多种修复方法
之前的修复无效
你并未完全理解问题

切勿跳过此流程的场景：

问题看似简单（简单Bug也有根本原因）
你时间紧迫（仓促行事必然导致返工）
经理要求立即修复（系统化调试比盲目尝试更快）

The Four Phases

四个阶段

You MUST complete each phase before proceeding to the next.

必须完成上一阶段后，才能进入下一阶段。

Phase 1: Root Cause Investigation

阶段1：根本原因调查

BEFORE attempting ANY fix:

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
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
Check Recent Changes
- What changed that could cause this?
- Git diff, recent commits
- New dependencies, config changes
- Environmental differences

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 ✗)

Trace Data Flow

WHEN error is deep in call stack:

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

在尝试任何修复前：

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
- 完整阅读堆栈跟踪信息
- 记录行号、文件路径、错误代码
稳定复现问题
- 你能否可靠地触发问题？
- 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
检查近期变更
- 哪些变更可能导致这个问题？
- Git diff、最近的提交
- 新增依赖、配置变更
- 环境差异

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：

针对每个组件边界：
  - 记录进入组件的数据
  - 记录离开组件的数据
  - 验证环境/配置的传递
  - 检查每一层的状态

运行一次以收集证据，确定问题出在哪个环节
然后分析证据，找出故障组件
再针对该组件进行调查

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： 哪一层出现故障（密钥 → 工作流 ✓，工作流 → 构建 ✗）

追踪数据流

当错误出现在调用栈深处时：

快速版流程：
- 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

阶段2：模式分析

Find the pattern before fixing:

Find Working Examples
- Locate similar working code in same codebase
- What works that's similar to what's broken?
Compare Against References
- If implementing pattern, read reference implementation COMPLETELY
- Don't skim - read every line
- Understand the pattern fully before applying
Identify Differences
- What's different between working and broken?
- List every difference, however small
- Don't assume "that can't matter"
Understand Dependencies
- What other components does this need?
- What settings, config, environment?
- What assumptions does it make?

修复前先找出规律：

寻找可用示例
- 在同一代码库中定位类似的可运行代码
- 哪些类似的代码可以正常运行？
与参考实现对比
- 如果是实现某种模式，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
识别差异
- 可运行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”
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

阶段3：假设与测试

Scientific method:

Form Single Hypothesis
- State clearly: "I think X is the root cause because Y"
- Write it down
- Be specific, not vague
Test Minimally
- Make the SMALLEST possible change to test hypothesis
- One variable at a time
- Don't fix multiple things at once
Verify Before Continuing
- Did it work? Yes → Phase 4
- Didn't work? Form NEW hypothesis
- DON'T add more fixes on top
When You Don't Know
- Say "I don't understand X"
- Don't pretend to know
- Ask for help
- Research more

遵循科学方法：

形成单一假设
- 清晰表述：“我认为X是根本原因，因为Y”
- 写下来
- 要具体，不要模糊
最小化测试
- 做出最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
验证后再继续
- 有效？是 → 进入阶段4
- 无效？形成新的假设
- 不要叠加更多修复
当你不确定时
- 直接说“我不理解X”
- 不要不懂装懂
- 寻求帮助
- 深入研究

Phase 4: Implementation

阶段4：实施修复

Fix the root cause, not the symptom:

Create Failing Test Case
- Simplest possible reproduction
- Automated test if possible
- One-off test script if no framework
- MUST have before fixing
Implement Single Fix
- Address the root cause identified
- ONE change at a time
- No "while I'm here" improvements
- No bundled refactoring
Verify Fix
- Test passes now?
- No other tests broken?
- Issue actually resolved?
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
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 the user before attempting more fixes

This is NOT a failed hypothesis - this is a wrong architecture.

修复根本原因，而非症状：

创建失败测试用例
- 最简单的复现方式
- 尽可能实现自动化测试
- 如果没有框架，可使用一次性测试脚本
- 修复前必须完成此步骤
实施单一修复
- 针对已确定的根本原因进行修复
- 一次只做一个变更
- 不要顺便做“其他改进”
- 不要捆绑重构
验证修复效果
- 测试现在通过了吗？
- 其他测试有没有被破坏？
- 问题真的解决了吗？
如果修复无效
- 停止操作
- 统计：你已经尝试了多少次修复？
- 如果 <3：回到阶段1，结合新信息重新分析
- 如果 ≥3：停止并质疑架构（见下文第5步）
- 未经架构讨论，不要尝试第4次修复
如果3次以上修复失败：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“大规模重构”才能实现
- 每次修复都会在其他地方引发新症状
停止并质疑基础问题：
- 这个模式从根本上合理吗？
- 我们是不是“因惯性而坚持”？
- 我们应该重构架构，还是继续修复症状？
在尝试更多修复前与用户讨论

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”
“试试改X看看行不行”
“同时做多个变更，然后运行测试”
“跳过测试，我手动验证就行”
“可能是X的问题，我来修复它”
“我不完全理解，但这个可能有用”
“模式要求X，但我要做些调整”
“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2次以上）
每次修复都会在不同位置暴露出新问题

以上所有情况都意味着：停止操作。回到阶段1。

如果3次以上修复失败：质疑架构（见阶段4.5）

Common Rationalizations

常见借口与实际情况

Excuse	Reality
"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	实际情况
“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的速度很快。
“紧急情况，没时间走流程”	系统化调试比盲目猜测更快。
“先试试这个，之后再调查”	第一次修复会定下模式。从一开始就做对。
“确认修复有效后再写测试”	未测试的修复无法持久。先写测试能验证修复效果。
“同时做多个修复更省时间”	无法确定哪个变更起作用。还会引入新Bug。
“参考内容太长，我调整一下模式”	一知半解必然导致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”（已经失败2次以上）	3次以上失败=架构问题。质疑模式，不要继续修复。

Quick Reference

快速参考

Phase	Key Activities	Success Criteria
1. Root Cause	Read errors, reproduce, check changes, gather evidence	Understand WHAT and WHY
2. Pattern	Find working examples, compare	Identify differences
3. Hypothesis	Form theory, test minimally	Confirmed or new hypothesis
4. Implementation	Create test, fix, verify	Bug resolved, tests pass

阶段	核心活动	成功标准
1. 根本原因调查	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	寻找可用示例、对比参考实现	识别差异
3. 假设与测试	形成理论、最小化测试	验证假设或形成新假设
4. 实施修复	创建测试用例、修复、验证	Bug解决，测试通过

Technique: Root Cause Tracing

技巧：根本原因追踪

When bugs manifest deep in the call stack, trace backward to find the original trigger.

当Bug出现在调用栈深处时，反向追踪找到最初的触发点。

The Tracing Process

追踪流程

Observe the Symptom

Error: git init failed in /Users/jesse/project/packages/core

Find Immediate Cause - What code directly causes this?

typescript

await execFileAsync('git', ['init'], { cwd: projectDir })

Ask: What Called This?

typescript

WorktreeManager.createSessionWorktree(projectDir, sessionId)
  → called by Session.initializeWorkspace()
  → called by Session.create()
  → called by test at Project.create()

Keep Tracing Up - What value was passed?
- ```
projectDir = ''
```
  (empty string!)
- Empty string as
```
cwd
```
  resolves to
```
process.cwd()
```

Find Original Trigger - Where did empty string come from?

typescript

const context = setupCoreTest() // Returns { tempDir: '' }
Project.create('name', context.tempDir) // Accessed before beforeEach!

观察症状

Error: git init failed in /Users/jesse/project/packages/core

找到直接原因 - 哪段代码直接导致了这个错误？
typescript
```
await execFileAsync('git', ['init'], { cwd: projectDir })
```

提问：是谁调用了这段代码？

typescript

WorktreeManager.createSessionWorktree(projectDir, sessionId)
  → called by Session.initializeWorkspace()
  → called by Session.create()
  → called by test at Project.create()

持续向上追踪 - 传入的值是什么？
- ```
projectDir = ''
```
  （空字符串！）
- 空字符串作为
```
cwd
```
  会解析为
```
process.cwd()
```

找到最初触发点 - 空字符串来自何处？

typescript

const context = setupCoreTest() // Returns { tempDir: '' }
Project.create('name', context.tempDir) // 在beforeEach之前就被访问了！

Adding Stack Traces

添加堆栈跟踪

When you can't trace manually, add instrumentation:

typescript

async function gitInit(directory: string) {
  const stack = new Error().stack
  console.error('DEBUG git init:', {
    directory,
    cwd: process.cwd(),
    nodeEnv: process.env.NODE_ENV,
    stack,
  })
  await execFileAsync('git', ['init'], { cwd: directory })
}

Tips:

Use
```
console.error()
```
in tests (logger may be suppressed)
Log before the dangerous operation, not after it fails
Include context: directory, cwd, environment variables
```
new Error().stack
```
shows complete call chain

当无法手动追踪时，添加工具代码：

typescript

async function gitInit(directory: string) {
  const stack = new Error().stack
  console.error('DEBUG git init:', {
    directory,
    cwd: process.cwd(),
    nodeEnv: process.env.NODE_ENV,
    stack,
  })
  await execFileAsync('git', ['init'], { cwd: directory })
}

提示：

在测试中使用
```
console.error()
```
（日志可能被屏蔽）
在危险操作前记录日志，而非失败后
包含上下文信息：目录、当前工作目录、环境变量
```
new Error().stack
```
会显示完整调用链

Finding Which Test Causes Pollution

找出导致污染的测试用例

If something appears during tests but you don't know which test, use bisection:

bash

undefined

如果测试过程中出现问题但不知道是哪个测试用例导致的，可使用二分法：

bash

undefined

Run tests one-by-one, stop at first polluter

逐个运行测试，在第一个导致污染的用例处停止

for f in src/**/*.test.ts; do npm test "$f" && [ -d .git ] && echo "POLLUTER: $f" && break done


**NEVER fix just where the error appears.** Trace back to find the original
trigger.

for f in src/**/*.test.ts; do npm test "$f" && [ -d .git ] && echo "POLLUTER: $f" && break done


**切勿仅在错误出现的位置修复。** 反向追踪找到最初的触发点。

Technique: Defense-in-Depth Validation

技巧：纵深防御验证

After finding root cause, validate at EVERY layer data passes through. Make the bug structurally impossible.

找到根本原因后，在数据流经的每一层都添加验证。从结构上杜绝Bug再次出现。

Why Multiple Layers

为什么需要多层验证

Single validation: "We fixed the bug"
Multiple layers: "We made the bug impossible"

Different layers catch different cases:

Entry validation catches most bugs
Business logic catches edge cases
Environment guards prevent context-specific dangers
Debug logging helps when other layers fail

单一验证：“我们修复了Bug”
多层验证：“我们让Bug无法出现”

不同层可捕获不同情况：

入口验证可捕获大多数Bug
业务逻辑可捕获边缘情况
环境防护可防止特定上下文下的危险操作
调试日志可在其他层失效时提供帮助

The Four Layers

四个层级

Layer 1: Entry Point Validation - Reject invalid input at API boundary

typescript

function createProject(name: string, workingDirectory: string) {
  if (!workingDirectory || workingDirectory.trim() === '') {
    throw new Error('workingDirectory cannot be empty')
  }
  if (!existsSync(workingDirectory)) {
    throw new Error(`workingDirectory does not exist: ${workingDirectory}`)
  }
}

Layer 2: Business Logic Validation - Ensure data makes sense for operation

typescript

function initializeWorkspace(projectDir: string, sessionId: string) {
  if (!projectDir) {
    throw new Error('projectDir required for workspace initialization')
  }
}

Layer 3: Environment Guards - Prevent dangerous operations in specific contexts

typescript

async function gitInit(directory: string) {
  if (process.env.NODE_ENV === 'test') {
    const normalized = normalize(resolve(directory))
    const tmpDir = normalize(resolve(tmpdir()))
    if (!normalized.startsWith(tmpDir)) {
      throw new Error(`Refusing git init outside temp dir during tests`)
    }
  }
}

Layer 4: Debug Instrumentation - Capture context for forensics

typescript

async function gitInit(directory: string) {
  logger.debug('About to git init', {
    directory,
    cwd: process.cwd(),
    stack: new Error().stack,
  })
}

层级1：入口点验证 - 在API边界拒绝无效输入

typescript

function createProject(name: string, workingDirectory: string) {
  if (!workingDirectory || workingDirectory.trim() === '') {
    throw new Error('workingDirectory cannot be empty')
  }
  if (!existsSync(workingDirectory)) {
    throw new Error(`workingDirectory does not exist: ${workingDirectory}`)
  }
}

层级2：业务逻辑验证 - 确保数据符合操作要求

typescript

function initializeWorkspace(projectDir: string, sessionId: string) {
  if (!projectDir) {
    throw new Error('projectDir required for workspace initialization')
  }
}

层级3：环境防护 - 防止在特定上下文中执行危险操作

typescript

async function gitInit(directory: string) {
  if (process.env.NODE_ENV === 'test') {
    const normalized = normalize(resolve(directory))
    const tmpDir = normalize(resolve(tmpdir()))
    if (!normalized.startsWith(tmpDir)) {
      throw new Error(`Refusing git init outside temp dir during tests`)
    }
  }
}

层级4：调试工具 - 捕获上下文信息用于取证

typescript

async function gitInit(directory: string) {
  logger.debug('About to git init', {
    directory,
    cwd: process.cwd(),
    stack: new Error().stack,
  })
}

Applying Defense-in-Depth

应用纵深防御

When you find a bug:

Trace the data flow - Where does bad value originate? Where used?
Map all checkpoints - List every point data passes through
Add validation at each layer - Entry, business, environment, debug
Test each layer - Try to bypass layer 1, verify layer 2 catches it

Don't stop at one validation point. Add checks at every layer.

当发现Bug时：

追踪数据流 - 错误值源自何处？在哪里被使用？
映射所有检查点 - 列出数据流经的每一个节点
在每一层添加验证 - 入口、业务逻辑、环境、调试
测试每一层 - 尝试绕过层级1，验证层级2能否捕获

不要仅停留在一个验证点。 在每一层都添加检查。

Real-World Impact

实际影响

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的概率：几乎为0 vs 频繁发生