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.

The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

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

The Feedback Loop Rule

The feedback loop is the debugging work. Before reading code to build a theory, create or identify a tight command that can go red on the user's exact symptom and green when the bug is fixed. A tight loop is fast, deterministic, agent-runnable, and specific enough to catch this bug — not merely "doesn't crash".

When a clean repro is hard, spend disproportionate effort building the loop.

When to Use

Use for ANY technical issue: test failures, bugs in production, unexpected behavior, performance problems, build failures, integration issues.

Use 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

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_file on the relevant source files. Use bash with grep to find the error string in the codebase.

2. Build a Tight Feedback Loop

• Can you trigger the user's exact symptom with one command?
• Does the command fail for this bug and only pass once the bug is fixed?
• Is it fast enough to run repeatedly?
• Is it deterministic?
• If not reproducible → gather more data, don't guess.

Ways to construct a loop — try in roughly this order:

1. Failing test at the seam that reaches the bug: unit, integration, or e2e.
2. HTTP script / curl against a running dev server.
3. CLI invocation with fixture input, diffing stdout/stderr against expected.
4. Headless browser script (Playwright/Puppeteer) asserting on DOM/console/network.
5. Replay a captured trace: HAR, request payload, event log, webhook body.
6. Throwaway harness that boots the smallest useful slice of the system.
7. Property / fuzz loop when the bug is intermittent wrong output.
8. Bisection harness suitable for git bisect run.
9. Differential loop comparing old vs new version, two configs, or two datasets.

Tighten the loop once it exists: make it faster, make the signal sharper (assert the exact symptom), make it more deterministic (pin time, seed randomness, isolate filesystem).

For non-deterministic bugs, the immediate goal is a higher reproduction rate, not perfection. Run the trigger 100x, parallelize, add stress, narrow timing windows. A 50% flake is debuggable; a 1% flake usually is not.

Action: Use bash to run the tight loop:

# Run a specific failing test
pytest tests/test_module.py::test_name -v

# Or run a scripted repro
python scripts/repro_bug.py

# Or run a high-repetition flaky repro
for i in {1..100}; do pytest tests/test_flake.py::test_name -q || break; done

3. Check Recent Changes

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

Action:

git log --oneline -10
git diff
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):

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. 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 bash with grep to trace references:

# Find where the function is called
grep -rn "function_name(" src/ --include="*.py"

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

Phase 1 Completion Checklist

• Error messages fully read and understood
• A tight loop command exists and has been run at least once
• Loop is red-capable: it asserts the user's exact symptom
• Loop is deterministic, or a flaky bug has a high enough reproduction rate
• Recent changes identified and reviewed
• Evidence gathered (logs, state, data flow)
• Problem isolated to specific component/code
• Root cause hypotheses can be stated and tested

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

Phase 2: Pattern Analysis

Find the pattern before fixing:

0. Minimize the Reproduction

Once the loop is red, shrink the repro to the smallest scenario that still goes red. Cut inputs, callers, config, data, and steps one at a time, re-running the loop after each cut. Keep only what is load-bearing for the failure.

A minimal repro narrows the hypothesis space and often becomes the cleanest regression test.

1. Find Working Examples

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

Action: Use bash with grep:

grep -rn "similar_pattern" src/ --include="*.py"

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 Ranked Falsifiable Hypotheses

• Generate 3–5 plausible hypotheses before testing any single one.
• Rank them by likelihood and cheapness to falsify.
• State the prediction each hypothesis makes: "If X is the cause, then changing or observing Y should make Z happen."
• Discard any hypothesis that does not make a testable prediction.

If the user is present, show the ranked list before testing. They may have domain knowledge that instantly re-ranks it.

2. Test Minimally

• Test the highest-ranked hypothesis with the smallest possible probe.
• Change one variable at a time.
• Don't fix multiple things at once.
• Prefer debugger/REPL inspection when available; one breakpoint beats ten logs.
• If you add logs, tag every temporary line with a unique prefix such as [DEBUG-a4f2] so cleanup is a single search.

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
• 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

# 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 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 the architecture vs. continue fixing symptoms?

Discuss with the user before attempting more fixes.

Red Flags — STOP and Follow Process

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"
• Proposing solutions before tracing data flow
• "One more fix attempt" (when already tried 2+)
• Each fix reveals a new problem in a different place

ALL of these mean: STOP. Return to Phase 1.

Common Rationalizations

Quick Reference

No shortcuts. No guessing. Systematic always wins.