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.** ## 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) - Manager 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 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 ### Phase 2: Pattern Analysis **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? ### Phase 3: Hypothesis and Testing **Scientific method:** 1. **Form Single Hypothesis** - State clearly: "I think X is the root cause because Y" - Write it down - Be specific, not vague ### Hypothesis Ranking For each hypothesis, score and document: | Hypothesis | Probability | Evidence | Falsification | Test Approach | |------------|-------------|----------|---------------|---------------| | H1: DB pool exhausted | 85% | Timeout errors every 30s | Check active connections < max | `SHOW PROCESSLIST` or pool metrics | | H2: Network latency | 30% | Intermittent not consistent | Ping DB host | `curl` timing, traceroute | **Scoring guide:** - 80-100%: Strong evidence, consistent with all symptoms → test first - 50-79%: Partial evidence, explains some symptoms → test second - <50%: Weak evidence, speculative → test only if higher-ranked fail Always state: "Highest-probability hypothesis is H1 (85%) because [evidence]." ### Five Whys Drill-Down Use when root cause is unclear after initial hypothesis. Ask "Why?" 5 times: **Example:** ``` Error: Database connection timeout after 30s Why? The database connection pool was exhausted Why? All connections were held by long-running queries Why? A new feature introduced N+1 query patterns Why? The ORM lazy-loading wasn't properly configured Why? Code review didn't catch the performance regression Root cause: Missing performance review criteria in PR checklist ``` Stop when you reach: a process failure, a human decision, or an external constraint. The answer to the 5th "Why?" is your fix target. 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 ### Phase 4: Implementation **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 - See `leverage-patterns.md` test-first protocol: write the test that defines success, then implement until it passes 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 the developer before attempting more fixes.** This is NOT a failed hypothesis — this is a wrong architecture. ### No-Progress Detection (catch a stuck loop before the 3rd blind retry) The "3 fixes failed" counter is a backstop, not the first signal. A loop is usually stuck several attempts earlier — detect it *structurally* instead of waiting to exhaust the count (loop-engineering no-progress signals): | Signal | What it looks like | Response | |---|---|---| | Repeated error | The SAME error recurs after your change — compare the **normalized** form (strip timestamps, IDs, paths, line offsets), not the raw text | Do not re-try the same class of fix. Form a genuinely new hypothesis (Phase 3) or escalate. | | Ping-pong edit | Your new diff reverts a previous attempt's diff (alternating file hashes) | Stop — you are oscillating between two wrong states. The real cause is elsewhere; escalate. | | Strategy repetition | You are about to run an approach already recorded as failed | Reject it without new evidence. A previously-failed strategy needs a NEW fact to be worth re-running. | | Verifier stagnation | Failing-test count / error signature does not improve across attempts | Treat as no progress even if each attempt "looks" different. | Rule of thumb: **a repeated normalized error signature with no new strategy = escalate now** — do not spend the remaining attempts. Record the normalized signature when you log an attempt so repeats are detectable across a long session (that is exactly what `ralph-state.local.md`'s `## Attempt counts` is for in an autonomous `/ralph-loop`). ## 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" - "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) ## Developer Signals You're Doing It Wrong **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. ## Common Rationalizations | 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. | ## Quick Reference | 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 | ## When Process Reveals "No Root Cause" 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. ## Supporting Techniques These techniques are 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:** - `leverage-patterns.md` test-first protocol — for creating failing test case (Phase 4, Step 1) - `.claude/skills/verification-before-completion/SKILL.md` — verify fix worked before claiming success ## Real-World Impact 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