systematic-debugging

# Systematic Debugging

## Overview

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.**

## The Iron Law

```
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
```

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

## When to Use

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)
- Someone wants it fixed NOW (systematic is faster than thrashing)

## The Four Phases

You MUST complete each phase before proceeding to the next.

---

## Phase 1: Root Cause Investigation

**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

**Action:** Use `read` on the relevant source files. Use `grep` to find the error string in the codebase.

### 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

**Action:** Use the `exec` tool to run the failing test or trigger the bug:

```bash
# Run specific failing test (Python example)
pytest tests/test_module.py::test_name -v

# Rust example
cargo test -p my-crate failing_test_name -- --nocapture

# Node example
npm test -- --grep "failing test"
```

### 3. Check Recent Changes

- What changed that could cause this?
- Git diff, recent commits
- New dependencies, config changes

**Action:**

```bash
# Recent commits
git log --oneline -10

# Uncommitted changes
git diff

# Changes in specific file
git log -p --follow src/problematic_file.py | head -100
```

### 4. Gather Evidence in Multi-Component Systems

**WHEN system has multiple components (API → service → database, CI → build → deploy):**

**BEFORE proposing fixes, add diagnostic instrumentation:**

For EACH component boundary:
- Log what data enters the component
- Log what data exits the component
- Verify environment/config propagation
- Check state at each layer

Run once to gather evidence showing WHERE it breaks.
THEN analyze evidence to identify the failing component.
THEN investigate that specific component.

### 5. Trace Data Flow

**WHEN error is deep in the call stack:**

- Where does the bad value originate?
- What called this function with the bad value?
- Keep tracing upstream until you find the source
- Fix at the source, not at the symptom

**Action:** Use `grep` to trace references:

```bash
# Find where the function is called
grep -rn "function_name(" src/

# Find where the variable is set
grep -rnE "variable_name\s*=" src/
```

### Phase 1 Completion Checklist

- [ ] Error messages fully read and understood
- [ ] Issue reproduced consistently
- [ ] Recent changes identified and reviewed
- [ ] Evidence gathered (logs, state, data flow)
- [ ] Problem isolated to specific component/code
- [ ] Root cause hypothesis formed

**STOP:** Do not proceed to Phase 2 until you understand WHY it's happening.

---

## Phase 2: Pattern Analysis

**Find the pattern before fixing:**

### 1. Find Working Examples

- Locate similar working code in the same codebase
- What works that's similar to what's broken?

**Action:** Use `grep` to find comparable patterns:

```bash
grep -rn "similar_pattern" src/
```

### 2. Compare Against References

- If implementing a pattern, read the 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?

---

## Phase 3: Hypothesis and Testing

**Scientific method:**

### 1. Form a 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 the hypothesis
- One variable at a time
- Don't fix multiple things at once

### 3. Verify Before Continuing

- Did it work? → 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 the user for help (use `ask_user_question` tool)
- Research more

---

## Phase 4: Implementation

**Fix the root cause, not the symptom:**

### 1. Create Failing Test Case

- Simplest possible reproduction
- Automated test if possible
- MUST have before fixing
- Use the `test-driven-development` skill

### 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

```bash
# Run the specific regression test
pytest tests/test_module.py::test_regression -v

# Run full suite — no regressions
pytest tests/ -q
```

### 4. If Fix Doesn't Work — The Rule of Three

- **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 an architectural problem:**
- Each fix reveals new shared state/coupling in a different pl