Cc Debugging

Skill: cc-debugging

STOP - Scientific Method Required

• Do NOT skip to FIX without STABILIZE → HYPOTHESIZE → EXPERIMENT
• ~50% of rushed fixes are wrong - Slow down under pressure
• Can you PREDICT when the bug occurs? If not, you don't understand it yet

CRISIS TRIAGE (2 minutes)

Production down? Use this prioritized subset:

Immediate (30 seconds each)

1. Can you reproduce it? If no → stabilize the error first (make it predictable)
2. What changed recently? Check recent commits, deployments, config changes
3. What do the logs say? Look at actual error messages, stack traces

Before Attempting Fix (60 seconds)

4. Do you understand the root cause? If no → don't guess, form a hypothesis first
5. Can you predict when the bug occurs? If no → you don't understand it yet

Why triage works: 90% of debugging is finding/understanding the bug. Only 10% is fixing it.

Feeling pressure to "just fix it"? That's exactly when you need to slow down. Rushed fixes are wrong ~50% of the time.

Key Definitions

Debugging

The process of identifying the root cause of an error and correcting it. Contrasts with testing (which detects errors initially).

Stabilizing an Error

Making an intermittent defect occur reliably/predictably. Cannot debug what you cannot reproduce.

Psychological Set

Seeing what you expect to see. Causes "debugging blindness" where programmers overlook defects because they expect the code to work.

Psychological Distance

The ease with which two items can be differentiated. Variables with small psychological distance (shiftrn/shiftrm) are easily confused.

Brute-Force Debugging

Techniques that are tedious but guaranteed to work. Use when quick approaches fail.

Scientific Debugging Flowchart

+------------------+
|  Error Reported  |
+--------+---------+
         |
         v
+------------------+
| 1. STABILIZE     | <-- Make error occur reliably
|    the error     |     Narrow to simplest test case
+--------+---------+
         |
         v
+------------------+
| 1b. PATTERN      | <-- How were similar bugs fixed?
|     REUSE GATE   |     Search codebase for patterns
+--------+---------+
         |
         v
+------------------+
| 2. Form          | <-- Based on available data + patterns
|    HYPOTHESIS    |     What could cause this?
+--------+---------+
         |
         v
+------------------+
| 3. Design        | <-- How will you prove/disprove?
|    EXPERIMENT    |     Code inspection? Test case?
+--------+---------+
         |
         v
+------------------+     +---------------+
| 4. Run test      |---->| Disproved?    |
|   PROVE/DISPROVE |     +-------+-------+
+--------+---------+             | Yes
         | Proved                v
         v                +---------------+
+------------------+      | Refine        |
| 5. FIX the       |      | hypothesis    |--+
|    defect        |      +---------------+  |
+--------+---------+                         |
         |                                   |
         v                                   |
+------------------+                         |
| 6. TEST the fix  |                         |
|    (verify!)     |                         |
+--------+---------+                         |
         |                                   |
         v                                   |
+------------------+                         |
| 7. LOOK for      |<------------------------+
|    similar bugs  |
+------------------+

CRITICAL: Do NOT skip steps. Do NOT jump to step 5 without completing 1-4.

Step 1b: Pattern Reuse Gate

BEFORE forming your hypothesis, search the codebase:

Questions to answer:

1. Has this bug (or similar) been fixed before in this codebase?
2. How do other parts of the code handle this situation?
3. Is there an established pattern I should follow?

If pattern found: Your hypothesis should account for it. Either:

• The pattern wasn't applied here (apply it)
• The pattern was applied but incorrectly (fix application)
• This is a new case the pattern doesn't cover (extend pattern)

If no pattern found: You're potentially establishing one. Be deliberate.

See: pattern-reuse-gate.md for full gate protocol.

Modes

CHECKER

Purpose: Audit debugging approach for systematic methodology Triggers:

• "am I debugging correctly"
• "review my debugging approach"
• "check my bug investigation"
• "audit debugging methodology" Non-Triggers:
• "help me find this bug" -> APPLIER
• "review my code" -> cc-routine-and-class-design Checklist: See checklists.md Output Format:

  Step	Status	Evidence	Issue
  Severity:

• VIOLATION: Skipped scientific method steps, made random changes, didn't verify fix
• WARNING: Incomplete stabilization, weak hypothesis, no similar-bug search
• PASS: Followed methodology systematically

APPLIER

Purpose: Guide systematic debugging when stuck on a bug Triggers:

• "help me debug this"
• "I can't find the bug"
• "why isn't this working"
• "figure out why this fails"
• "investigate this error" Non-Triggers:
• "review my debugging process" -> CHECKER
• "is my error handling good" -> cc-defensive-programming Produces:
• Stabilization strategy for intermittent bugs
• Hypothesis formation guidance
• Binary search narrowing approach
• Fix verification recommendations Constraints:
• Must stabilize before hypothesizing
• Must have hypothesis before fixing
• Must verify fix before committing
• Must search for similar bugs after fixing

Decision Flowcharts

Quick vs Systematic Debugging

digraph quick_vs_systematic {
    rankdir=TB;

    START [label="Bug encountered" shape=doublecircle];
    obvious [label="Is cause obvious?\n(< 5 min investigation)" shape=diamond];
    quick [label="Quick fix\n(with verification)" shape=box style=filled fillcolor=lightgreen];
    timer [label="Set TIME LIMIT\n(15-30 minutes)" shape=box style=filled fillcolor=lightyellow];
    found [label="Found bug?" shape=diamond];
    systematic [label="Switch to\nSYSTEMATIC debugging" shape=box style=filled fillcolor=lightblue];
    done [label="Fix + Verify + Search similar" shape=box style=filled fillcolor=lightgreen];

    START -> obvious;
    obvious -> quick [label="yes"];
    obvious -> timer [label="no"];
    quick -> done;
    timer -> found;
    found -> done [label="yes"];
    found -> systematic [label="no, time expired"];
}

Hypothesis Formation

digraph hypothesis_formation {
    rankdir=TB;

    START [label="Need hypothesis" shape=doublecircle];
    data [label="Gather ALL available data\n(logs, repro steps, user reports)" shape=box];
    patterns [label="Look for patterns\n(when it happens, when it doesn't)" shape=box];
    changes [label="What changed recently?\n(code, config, data, environment)" shape=box];
    hypothesize [label="Form hypothesis:\n'The bug is caused by X'" shape=diamond style=filled fillcolor=lightyellow];
    predict [label="Can you PREDICT\nwhen bug occurs?" shape=diamond];
    refine [label="Refine hypothesis\nwith more data" shape=box];
    experiment [label="Design experiment\nto prove/disprove" shape=box style=filled fillcolor=lightgreen];

    START -> data;
    data -> patterns;
    patterns -> changes;
    changes -> hypothesize;
    hypothesize -> predict;
    predict -> experiment [label="yes"];
    predict -> refine [label="no"];
    refine -> hypothesize;
}

Anti-Patterns

Debug by Superstition

Pattern: Blaming the compiler, machine, framework, or external factors. Reality: If you wrote it, it's your fault. Programs don't do something different every time. Counter: Assume the error is yours. Take responsibility. This helps debugging AND credibility.

Random Changes (Voodoo Programming)

Pattern: "I'll just put a -1 here and try it" without understanding why. Reality: You learn nothing. The more you change without understanding, the less confidence you'll have. Counter: Be confident the change will work BEFORE making it.

Special-Case Fixes (CODING HORROR)

Pattern: if (client == 45) { sum[45] = sum[45] + 3.45; } to "fix" a calculation error. Reality: Won't work (initialization bugs are unpredictable), unmaintainable, creates barnacles. Counter: Fix the root cause in the algorithm. Never add special cases for specific values.

Fix Symptom Not Cause

Pattern: Making the error message go away without understanding why it appeared. Reality: Bug still exists. Will manifest differently later, harder to find. Counter: Understand completely before fixing. Predict when bug occurs.

Skip Verification Under Pressure

Pattern: Committing fix before skiing trip without testing. Reality: ~50% of fixes are wrong the first time (Yourdon 1986b). Counter: ALWAYS verify. No exceptions. "It might take more time, but it'll probably take less."

Ignore Compiler Warnings

Pattern: Suppressing warnings instead of fixing them. Reality: "Closing your eyes doesn't make the adult go away." Counter: Fix every warning. Treat warnings as errors.

Red Flags - STOP and Apply Scientific Method

Tempted to Skip Stabilization:

• "It's intermittent but I have a theory"
• "I saw it happen once, that's enough"
• "I'll just try this fix and see"

Hypothesis-Free Zone:

• "I have no idea what's causing this"
• "Let me just add some print statements"
• "I'll comment out code until it works"

Blaming Externals:

• "It must be the compiler"
• "Works on my machine"
• "Something must have changed in the environment"

Pressure-Induced Shortcuts:

• "I just need to ship this"
• "I'll investigate properly later"
• "It's probably fine"

All of these mean: Return to step 1. Stabilize. Hypothesize. Experiment.

Rationalization Counters

Pressure Testing Scenarios

Scenario 1: Intermittent Failure

Situation: Test fails randomly, maybe 1 in 10 runs. Wrong Response: "I'll just run it again until it passes." REQUIRED Response: Stabilize first. Common causes: initialization error, timing issue, dangling pointer. Find the pattern.

Scenario 2: Production Emergency

Situation: App down in prod. Pressure to "just fix it." Wrong Response: Make change, deploy immediately without testing. REQUIRED Response: Take breath. Form hypothesis. Verify fix works. ~50% of rushed fixes are wrong.

Scenario 3: "It Works Now"

Situation: Bug disappeared after making some changes. Wrong Response: "Great, it's fixed!" Ship it. REQUIRED Response: Do you understand WHY it's fixed? If not, you haven't fixed it - you've hidden it.

Scenario 4: Cannot Reproduce

Situation: User reports bug you can't reproduce. Wrong Response: "Works on my machine. Close ticket." REQUIRED Response: Replicate user's environment. Get more data. The bug is real.

Scenario 5: Deep in Code

Situation: Been debugging same issue for hours, getting frustrated. Wrong Response: Keep staring at same code. Try random changes. REQUIRED Response: Take a break. Talk to rubber duck. Look at completely different area.

Binary Search for Bug Location

When defect location is unknown within large code region:

1. Use divide and conquer
2. Remove about half the code first time
3. Does bug persist? If yes → defect is in remaining half
4. Divide that section in half
5. Continue until defect found

Methods:

• Comment out calls to routines
• Use preprocessor to remove code
• Set breakpoint partway through
• Skip execution of certain routines in debugger

When to use: Defect in large region (>50 lines). Don't use for already-localized bugs.

Brute-Force Techniques

When systematic debugging hasn't found the bug, use these guaranteed (but tedious) approaches:

• Perform full design/code review on broken code
• Throw away section and redesign/recode from scratch
• Compile with full debugging information
• Compile at pickiest warning level, fix ALL warnings
• Create unit test harness, test in isolation
• Create automated test suite, run all night
• Step through big loop manually until error
• Instrument with logging statements
• Compile with different compiler
• Compile/run in different environment
• Use special libraries that warn on misuse
• Replicate end-user's full machine configuration
• Integrate new code in small pieces

Fix Verification Procedure

Before committing ANY fix:

1. Triangulate: Find cases that SHOULD and SHOULDN'T trigger bug
2. Predict correctly every time before attempting fix
3. Understand vicinity (~hundreds of lines, not just bug site)
4. Confirm diagnosis - rule out competing hypotheses FIRST
5. Save original source before changes
6. Fix root cause - no special-case workarounds
7. One change at a time - never batch
8. Run triangulation test cases again
9. Run full regression tests
10. Add unit test that exposes the defect (prevents regression)
11. Search for similar defects - defects cluster

Evidence Summary

Chain