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_file` on the relevant source files. Use `search_files` 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 `terminal` tool to run the failing test or trigger the bug: ```bash # Run specific failing test pytest tests/test_module.py::test_name -v # Run with verbose output pytest tests/test_module.py -v --tb=long ``` ### 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 `search_files` to trace references: ```python # Find where the function is called search_files("function_name(", path="src/", file_glob="*.py") # Find where the variable is set search_files("variable_name\\s*=", path="src/", file_glob="*.py") ``` ### 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 `search_files` to find comparable patterns: ```python search_files("similar_pattern", path="src/", file_glob="*.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 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 - 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 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.** This is NOT a failed hypothesis — this is a wrong architecture. --- ## 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 a new problem in a different place** **ALL of these mean: STOP. Return to Phase 1.** **If 3+ fixes failed:** Question the architecture (Phase 4 step 5). ## 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 the pattern, don't fix again. | ## Quick Reference | Phase | Key Activities | Success Criteria | |-------|---------------|------------------| | **1. Root Cause** | Read errors, reproduce, check changes, gather evidence, trace data flow | Understand WHAT and WHY | | **2. Pattern** | Find working examples, compare, identify differences | Know what's different | | **3. Hypothesis** | Form theory, test minimally, one variable at a time | Confirmed or new hypothesis | | **4. Implementation** | Create regression test, fix root cause, verify | Bug resolved, all tests pass | ## the agent Integration ### Investigation Tools Use these the agent tools during Phase 1: - **`search_files`** — Find error strings, trace function calls, locate patterns - **`read_file`** — Read source code with line numbers for precise analysis - **`terminal`** — Run tests, check git history, reproduce bugs - **`web_search`/`web_extract`** — Research error messages, library docs ### With delegate_task For complex multi-component debugging, dispatch investigation subagents: ```python delegate_task( goal="Investigate why [specific test/behavior] fails", context=""" Follow systematic-debugging skill: 1. Read the error message carefully 2. Reproduce the issue 3. Trace the data flow to find root cause 4. Report findings — do NOT fix yet Error: [paste full error] File: [path to failing code] Test command: [exact command] """, toolsets=['terminal', 'file'] ) ``` ### With test-driven-development When fixing bugs: 1. Write a test that reproduces the bug (RED) 2. Debug systematically to find root cause 3. Fix the root cause (GREEN) 4. The test proves the fix and prevents regression ## 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 **No shortcuts. No guessing. Systematic always wins.**