refactor-decompose-function

name	refactor-decompose-function
description	For long functions: break into smaller pieces, extract helper functions, reduce nesting, improve testability and readability.

refactor-decompose-function

When to Use

• Function is longer than 10-15 lines
• Multiple levels of nesting
• Hard to understand at a glance
• Difficult to test parts independently
• Code has natural "chunks" with different purposes

When NOT to Use

• Function is already simple and clear
• Decomposition would obscure the algorithm
• Helpers would only be used once with no clarity gain

The Pattern

Extract cohesive chunks into named helper functions. Each function should do one thing.

# BEFORE: Long, hard to test
def process_data(data):
    # Validate
    if not data:
        raise ValueError("Empty data")
    if not all(isinstance(x, int) for x in data):
        raise TypeError("Non-integer data")

    # Transform
    result = []
    for x in data:
        if x > 0:
            result.append(x * 2)
        else:
            result.append(0)

    # Summarize
    total = sum(result)
    avg = total / len(result)
    return {'data': result, 'total': total, 'average': avg}

# AFTER: Decomposed, each part testable
def process_data(data):
    validate(data)
    transformed = transform(data)
    return summarize(transformed)

def validate(data):
    if not data:
        raise ValueError("Empty data")
    if not all(isinstance(x, int) for x in data):
        raise TypeError("Non-integer data")

def transform(data):
    return [x * 2 if x > 0 else 0 for x in data]

def summarize(data):
    total = sum(data)
    return {'data': data, 'total': total, 'average': total / len(data)}

Example (from pytudes)

# Spelling correction (spell.py) - beautifully decomposed
def correction(word):
    """Most probable spelling correction for word."""
    return max(candidates(word), key=P)

def candidates(word):
    """Generate possible spelling corrections for word."""
    return known([word]) or known(edits1(word)) or known(edits2(word)) or [word]

def known(words):
    """The subset of words that appear in the dictionary."""
    return set(w for w in words if w in WORDS)

def edits1(word):
    """All edits one edit away from word."""
    letters = 'abcdefghijklmnopqrstuvwxyz'
    splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
    deletes = [L + R[1:] for L, R in splits if R]
    transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]
    replaces = [L + c + R[1:] for L, R  splits  R  c  letters]
    inserts = [L + c + R  L, R  splits  c  letters]
     (deletes + transposes + replaces + inserts)

 ():
    
     (e2  e1  edits1(word)  e2  edits1(e1))

Key Principles

1. Each function = one job: Name describes the job
2. 1-5 lines ideal: If longer, consider splitting
3. Testable units: Each helper can be tested alone
4. Read top-down: High-level function shows the story
5. Compose, don't nest: f(g(h(x))) beats deeply nested code