Systematic Debugging

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

code

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

Python:

python

# Read the full traceback
Traceback (most recent call last):
  File "/app/users.py", line 45, in create_user
    db.execute(query, params)
  File "/app/database.py", line 23, in execute
    cursor.execute(sql, params)
sqlite3.IntegrityError: UNIQUE constraint failed: users.email
#                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#                        This tells you EXACTLY what's wrong

Flutter:

dart

// Read the full exception
Exception: Bad state: Cannot add new events after calling close
#0      _BroadcastStreamController.add (dart:async/broadcast_stream_controller.dart:248)
#1      UserBloc.addUser (package:app/blocs/user_bloc.dart:45)
#                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#                        Line 45 in user_bloc.dart is the issue

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

Python:

bash

# Make it reproducible
pytest tests/test_user.py::test_create_user -v
# Does it fail every time? Or just sometimes?

# If flaky, run multiple times
for i in {1..10}; do pytest tests/test_user.py::test_create_user; done

Flutter:

bash

# Make it reproducible
flutter test test/user_bloc_test.dart --name "creates user"

# If flaky, run multiple times
for i in {1..10}; do flutter test test/user_bloc_test.dart; done

3. Check Recent Changes

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

bash

# What changed recently?
git log --oneline -10
git diff HEAD~5

# What dependencies changed?
git diff HEAD~5 requirements.txt  # Python
git diff HEAD~5 pubspec.yaml      # Flutter

4. Gather Evidence in Multi-Component Systems

WHEN system has multiple components (API → service → database, UI → BLoC → repository):

BEFORE proposing fixes, add diagnostic instrumentation:

code

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

Python example (multi-layer system):

python

# Layer 1: API endpoint
@app.post("/users")
def create_user(user_data: dict):
    print(f"=== API received: {user_data}")
    result = user_service.create(user_data)
    print(f"=== API returning: {result}")
    return result

# Layer 2: Service
class UserService:
    def create(self, data: dict):
        print(f"=== Service received: {data}")
        user = User(**data)
        print(f"=== Service created user: {user}")
        saved = self.repository.save(user)
        print(f"=== Service saved: {saved}")
        return saved

# Layer 3: Repository
class UserRepository:
    def save(self, user: User):
        print(f"=== Repository saving: {user.dict()}")
        print(f"=== DB state: {self.db.is_connected}")
        result = self.db.insert("users", user.dict())
        print(f"=== Repository result: {result}")
        return result

Flutter example (BLoC pattern):

dart

// Layer 1: Widget
ElevatedButton(
  onPressed: () {
    debugPrint('=== Widget: Adding user event');
    context.read<UserBloc>().add(AddUserEvent(user));
  },
)

// Layer 2: BLoC
class UserBloc extends Bloc<UserEvent, UserState> {
  Future<void> _onAddUser(AddUserEvent event, Emitter<UserState> emit) async {
    debugPrint('=== BLoC received: ${event.user}');
    debugPrint('=== BLoC state before: $state');
    
    final result = await repository.createUser(event.user);
    
    debugPrint('=== BLoC result: $result');
    emit(UserCreated(result));
  }
}

// Layer 3: Repository
class UserRepository {
  Future<User> createUser(User user) async {
    debugPrint('=== Repository creating: ${user.toJson()}');
    final response = await httpClient.post('/users', body: user.toJson());
    debugPrint('=== Repository response: ${response.statusCode}');
    return User.fromJson(response.body);
  }
}

5. Trace Data Flow

WHEN error is deep in call stack:

See root-cause-tracing.md 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?

Python:

python

# Find similar working test
# Working: test_create_user_with_email()
# Broken:  test_create_user_without_email()
# What's different?

Flutter:

dart

// Find similar working widget test
// Working: testWidgets('creates user with valid data')
// Broken:  testWidgets('creates user with empty email')
// What's different?

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

Example:

code

Hypothesis: User creation fails because email validation runs before
the email field is set, causing it to reject empty string.

Evidence: Logs show validator called with email='' before
user.email is assigned.

2. Test Minimally

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

Python:

python

# Test hypothesis: validation order issue
# Change ONLY the order, nothing else
def create_user(data: dict):
    user = User(**data)  # Create first
    user.validate()      # Then validate (was: validate first)
    return user

Flutter:

dart

// Test hypothesis: state emission order
// Change ONLY the order
void _onAddUser(AddUserEvent event, Emitter<UserState> emit) async {
  emit(UserLoading());     // Was after repository call
  final result = await repository.createUser(event.user);
  emit(UserCreated(result));
}

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
•Use the test-driven-development skill for writing proper failing tests

Python:

python

def test_creates_user_with_empty_email():
    """Regression test for issue #123."""
    user_data = {"name": "Alice", "email": ""}
    
    with pytest.raises(ValidationError, match="Email required"):
        create_user(user_data)

Flutter:

dart

test('creates user with empty email', () {
  final user = User(name: 'Alice', email: '');
  
  expect(
    () => userBloc.add(AddUserEvent(user)),
    throwsA(isA<ValidationError>()),
  );
});

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?

Python:

bash

# Run the specific test
pytest tests/test_user.py::test_creates_user_with_empty_email -v

# Run all tests to check for regressions
pytest tests/ -v

Flutter:

bash

# Run the specific test
flutter test test/user_bloc_test.dart --name "creates user with empty email"

# Run all tests
flutter test

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 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)

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	Actions	Outcome
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

Python-Specific Debugging Tools

Using pdb (Python debugger):

python

import pdb; pdb.set_trace()  # Python < 3.7
breakpoint()                  # Python >= 3.7

# Common pdb commands:
# l(ist)    - Show current code
# n(ext)    - Next line
# s(tep)    - Step into function
# c(ontinue) - Continue execution
# p variable - Print variable
# pp variable - Pretty-print variable

Logging for diagnosis:

python

import logging
logging.basicConfig(level=logging.DEBUG)

logger = logging.getLogger(__name__)
logger.debug(f"Variable state: {var}")
logger.debug(f"Function called with: {args}, {kwargs}")

pytest debugging:

bash

# Show print statements
pytest -s

# Drop into debugger on failure
pytest --pdb

# Show local variables on failure
pytest -l

# Verbose output
pytest -vv

Flutter-Specific Debugging Tools

Using debugger:

dart

// Add breakpoint in IDE or use debugger;
void someFunction() {
  debugger();  // Execution pauses here when running in debug mode
  // ...
}

Logging for diagnosis:

dart

import 'package:flutter/foundation.dart';

debugPrint('Variable state: $var');
debugPrint('Function called with: ${widget.toString()}');

// Conditional logging
if (kDebugMode) {
  print('Debug only: $sensitiveData');
}

Flutter DevTools:

bash

# Launch DevTools
flutter pub global activate devtools
flutter pub global run devtools

# Then run app in debug mode
flutter run

Widget inspection:

dart

// Dump widget tree to console
debugDumpApp();

// Dump render tree
debugDumpRenderTree();

// Dump layer tree
debugDumpLayerTree();

Supporting Techniques

See references/ directory for detailed techniques:

•root-cause-tracing.md - Trace bugs backward through call stack to find original trigger
•condition-based-waiting.md - Replace arbitrary timeouts with condition polling for async operations

Related skills:

•test-driven-development - For creating failing test case (Phase 4, Step 1)

When Process Reveals "No Root Cause"

If systematic investigation reveals issue is truly environmental, timing-dependent, or external:

•You've completed the process
•Document what you investigated
•Implement appropriate handling (retry, timeout, error message)
•Add monitoring/logging for future investigation

But: 95% of "no root cause" cases are incomplete investigation.

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