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)
• 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

Shannon tracking: Record reproduction steps in Serena:

serena.write_memory(f"debugging/{bug_id}/reproduction", {
    "steps": ["Step 1", "Step 2", "Step 3"],
    "reproducible": True/False,
    "frequency": "100%" / "50%" / "intermittent",
    "timestamp": ISO_timestamp
})

3. Check Recent Changes

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

Shannon integration: Use git log with metrics:

git log --since="1 week ago" --oneline --stat
# Track: files_changed, lines_added, complexity_diff

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):

# 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 ✗)

Shannon tracking: Save diagnostic results:

serena.write_memory(f"debugging/{bug_id}/diagnostics", {
    "layer_results": {
        "layer1": {"status": "PASS", "evidence": "..."},
        "layer2": {"status": "FAIL", "evidence": "..."},
    },
    "failing_layer": "layer2",
    "timestamp": ISO_timestamp
})

5. Trace Data Flow

WHEN error is deep in call stack:

REQUIRED SUB-SKILL: Use shannon:root-cause-tracing for 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

Shannon enhancement: Quantitative trace tracking:

serena.write_memory(f"debugging/{bug_id}/trace", {
    "trace_depth": 5,  # How many layers traced
    "root_cause_layer": "user_input_validation",
    "trace_path": ["handler", "service", "validator", "input", "user"],
    "time_to_trace": "15 minutes",
    "timestamp": ISO_timestamp
})

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?

Shannon integration: Use codebase_search:

# Find working examples automatically
working_examples = codebase_search(
    query="working implementation of authentication",
    target_directories=["src/"]
)

2. Compare Against References

• If implementing pattern, read reference implementation COMPLETELY
• Don't skim - read every line (use forced-reading-protocol skill)
• Understand the pattern fully before applying

Shannon requirement: Must use forced-reading-protocol for reference docs

3. Identify Differences

• What's different between working and broken?
• List every difference, however small
• Don't assume "that can't matter"

Shannon tracking: Quantify differences:

differences = {
    "config_differences": 3,
    "code_differences": 7,
    "dependency_differences": 2,
    "total_diff_lines": 45
}

4. Understand Dependencies

• What other components does this need?
• What settings, config, environment?
• What assumptions does it make?

Shannon integration: Use mcp-discovery skill to find relevant MCPs:

# Need database debugging? Find db MCPs
relevant_mcps = mcp_discovery.find_for_domain("database debugging")

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

Shannon requirement: Quantify confidence:

hypothesis = {
    "theory": "Missing environment variable causes auth failure",
    "reasoning": "Layer 2 diagnostics show IDENTITY unset",
    "confidence": 0.85,  # 85% confidence (0.00-1.00)
    "evidence_strength": "STRONG",  # Based on diagnostic evidence
    "timestamp": ISO_timestamp
}

serena.write_memory(f"debugging/{bug_id}/hypothesis", hypothesis)

2. Test Minimally

• Make the SMALLEST possible change to test hypothesis
• One variable at a time
• Don't fix multiple things at once

Shannon tracking: Record test result:

test_result = {
    "hypothesis_id": hypothesis_id,
    "change_made": "Set IDENTITY env var in workflow",
    "outcome": "SUCCESS" / "FAILURE",
    "confidence_update": 0.95 if success else 0.10,
    "timestamp": ISO_timestamp
}

3. Verify Before Continuing

• Did it work? Yes → Phase 4
• Didn't work? Form NEW hypothesis
• DON'T add more fixes on top

Shannon enhancement: Track attempt count:

attempts = get_attempt_count(bug_id)  # From Serena

if attempts >= 3:
    # CRITICAL: Question architecture (see Phase 4.5 below)
    alert_architectural_smell(bug_id)

4. When You Don't Know

• Say "I don't understand X"
• Don't pretend to know
• Ask for help
• Research more

Shannon integration: Use research MCPs:

# Use Tavily for best practices
research = tavily.search("best practices for X")

# Use Context7 for library docs
docs = context7.get_library_docs("library/name")

# Save research to Serena
serena.write_memory(f"debugging/{bug_id}/research", research)

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
• REQUIRED SUB-SKILL: Use shannon:test-driven-development for proper failing tests (with REAL systems, NO MOCKS)

Shannon requirement: Test must use real systems:

// ✅ GOOD: Real system test
test('auth fails with missing env var', async () => {
  // DON'T mock - use real auth system
  const result = await realAuthSystem.authenticate(credentials);
  expect(result.error).toBe('Missing IDENTITY');
});

// ❌ BAD: Mocked test
test('auth fails', () => {
  const mock = jest.fn().mockRejection('error');
  // This doesn't prove real system behavior
});

2. Implement Single Fix

• Address the root cause identified
• ONE change at a time
• No "while I'm here" improvements
• No bundled refactoring

Shannon tracking: Record fix details:

fix = {
    "root_cause": "Missing IDENTITY env var",
    "fix_applied": "Added IDENTITY to workflow secrets",
    "files_changed": 1,
    "lines_changed": 3,
    "fix_type": "configuration",
    "timestamp": ISO_timestamp
}

3. Verify Fix

• Test passes now?
• No other tests broken?
• Issue actually resolved?

Shannon requirement: Use verification-before-completion skill:

MANDATORY: Run all 3 validation tiers
- Tier 1 (Flow): Code compiles
- Tier 2 (Artifacts): Tests pass
- Tier 3 (Functional): Real system verification

NO claims without verification evidence.

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

Shannon tracking: Architectural smell detection:

debugging_history = serena.query_memory(f"debugging/{bug_id}/*")

attempts = len([h for h in debugging_history if h["type"] == "fix_attempt"])

if attempts >= 3:
    pattern = analyze_failure_pattern(debugging_history)

    architectural_smell = {
        "attempts": attempts,
        "pattern": pattern,  # "each_fix_reveals_new_problem" etc
        "recommendation": "REFACTOR_ARCHITECTURE",
        "confidence": 0.95,
        "timestamp": ISO_timestamp
    }

    serena.write_memory(f"debugging/{bug_id}/architectural_smell", architectural_smell)

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 your human partner 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 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)

Your Human Partner's 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.

Shannon Enhancement: Quantitative Metrics

Track debugging metrics in Serena:

debugging_session = {
    "bug_id": bug_id,
    "start_time": start_timestamp,
    "end_time": end_timestamp,
    "duration_minutes": 45,

    # Phase completion
    "phases_completed": ["investigation", "analysis", "hypothesis", "implementation"],

    # Quantitative metrics
    "reproduction_attempts": 3,
    "reproduction_success": True,
    "trace_depth": 5,
    "hypotheses_tested": 2,
    "fix_attempts": 1,
    "success": True,

    # Pattern recognition
    "similar_bugs_resolved": 3,  # From Serena history
    "root_cause_category": "configuration",
    "architectural_smell": False,

    # Evidence quality
    "diagnostic_evidence_strength": "STRONG",
    "hypothesis_confidence": 0.95,
    "fix_verification": "3/3 tiers PASS",

    "timestamp": ISO_timestamp
}

serena.write_memory(f"debugging/sessions/{session_id}", debugging_session)

Pattern learning over time:

# Query historical debugging data
auth_bugs = serena.query_memory("debugging/*/root_cause_category:auth")

# Learn patterns
pattern = {
    "category": "authentication",
    "total_bugs": len(auth_bugs),
    "avg_resolution_time": average([b["duration_minutes"] for b in auth_bugs]),
    "common_root_causes": {
        "missing_env_vars": 12,  # 80% of auth bugs
        "expired_tokens": 2,
        "config_mismatch": 1
    },
    "architectural_refactors_triggered": 0,  # No refactors yet
    "recommendation": "Add env var validation at startup"
}

Quick Reference

Phase	Key Activities	Success Criteria	Shannon Enhancement
1. Root Cause	Read errors, reproduce, check changes, gather evidence	Understand WHAT and WHY	Quantitative tracking in Serena
2. Pattern	Find working examples, compare	Identify differences	Automated pattern search
3. Hypothesis	Form theory, test minimally	Confirmed or new hypothesis	Confidence scoring (0.00-1.00)
4. Implementation	Create test, fix, verify	Bug resolved, tests pass	3-tier validation gates

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 (save to Serena)
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.

Integration with Other Skills

This skill requires using:

• root-cause-tracing - REQUIRED when error is deep in call stack (see Phase 1, Step 5)
• test-driven-development - REQUIRED for creating failing test case (see Phase 4, Step 1)
• verification-before-completion - REQUIRED for verifying fix worked (see Phase 4, Step 3)

Complementary skills:

• defense-in-depth - Add validation at multiple layers after finding root cause
• mcp-discovery - Find debugging MCPs for specific domains

Shannon integration:

• Serena MCP - Track all debugging sessions, learn patterns
• Sequential MCP - Deep analysis when stuck (100+ thoughts)
• Tavily/Context7 - Research when don't understand issue

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

Shannon tracking proves this: Query debugging_sessions with success:true vs failed attempts.

The Bottom Line

Systematic > Random. Evidence > Guessing. Root Cause > Symptom.

Shannon's quantitative tracking turns debugging from art into science.

Measure everything. Learn from patterns. Never fix without understanding.