Refactoring Patterns Framework

A disciplined approach to improving the internal structure of existing code without changing its observable behavior. Every refactoring follows the same loop: verify tests pass, apply one small structural change, verify tests still pass.

Core Principle

Refactoring is not rewriting. It is a sequence of small, behavior-preserving transformations, each backed by tests. You never change what the code does — only how it is organized. Big-bang rewrites fail because they combine structural change with behavioral change, making it impossible to know which broke things.

The foundation: Bad code is a natural consequence of delivering under time pressure, not a character flaw. Code smells are objective signals of degraded structure; the smell catalog tells you where to look, and the refactoring catalog tells you what to do.

Scoring

Goal: 10/10. Score structural quality by how many of the eight Quick Diagnostic rows pass — score = round(passed / 8 × 10), adjusting down when a single smell is severe. Bands:

• 9-10: no obvious smells remain, each function does one thing, names reveal intent, duplication is eliminated, conditionals use polymorphism where apt, and tests cover the refactored paths.
• 5-6: a few smells remain (a Long Method, some duplication) but structure is mostly sound.
• ≤3: pervasive smells — tangled conditionals, God classes, duplication everywhere — or no tests to refactor safely.

Always state the current score, name the smells driving it down, and list the specific refactorings needed to reach 10/10.

The Refactoring Patterns Framework

Six areas of focus for systematically improving code structure:

1. Code Smells as Triggers

Core concept: Code smells are surface indicators of deeper structural problems — not bugs, but signals that the design makes code harder to understand, extend, or maintain. Each smell maps to named refactorings that fix it.

Why it works: Named smells give teams objective criteria instead of subjective "I don't like this" — "This is Feature Envy" points directly at the fix.

Key insights:

• Smells cluster into five families: Bloaters, Object-Orientation Abusers, Change Preventers, Dispensables, Couplers
• Long Method is the most common smell; Duplicate Code is the most expensive
• A method that needs a comment to explain what it does is a smell — extract and name the block instead
• Shotgun Surgery (one change, many classes) and Divergent Change (one class, many reasons to change) are opposite signals of misplaced responsibilities
• Primitive Obsession — raw strings/ints instead of small domain objects — spreads errors and duplication

Code applications:

Context	Pattern	Example
Method > 10 lines	Extract Method	Pull loop body into calculateLineTotal()
One change touches many classes (Shotgun Surgery)	Move Method/Field	Gather the scattered behavior into one class
Same params in many methods	Introduce Parameter Object	startDate, endDate → DateRange
Copy-pasted logic	Extract Method + Pull Up Method	Share via common method or base class

See references/smell-catalog.md when you need to name a smell and its fix — all five families (Bloaters, OO Abusers, Change Preventers, Dispensables, Couplers) with detection heuristics and the refactoring each maps to.

2. Composing Methods

Core concept: Most refactoring starts here: break long methods into smaller, well-named pieces that read like prose — high-level steps delegating to clearly named helpers.

Why it works: Short methods with intention-revealing names eliminate comments, make bugs obvious at a glance, and enable reuse; a method call costs nothing to read when the name says everything.

Key insights:

• Extract Method is the single most important refactoring — master it first
• Urge to write a comment? Extract the block and use the comment as the method name
• Inline Method when the body is as clear as the name — indirection without value is noise
• Replace Temp with Query for computed values used in multiple places; Split Temporary Variable when one temp serves two purposes
• Replace Method with Method Object when locals are too tangled to extract — they become fields

Code applications:

Context	Pattern	Example
Block with a comment	Extract Method	// check eligibility → isEligible()
Temp used once	Inline Variable	Drop const price = order.getPrice()
Trivial delegating method	Inline Method	Inline return deliveries > 5 if used once
Method with many tangled locals	Replace Method with Method Object	Locals become fields in a new class

See references/composing-methods.md when applying any method-level transformation — step-by-step mechanics and before/after code for Extract/Inline Method, Extract/Inline Variable, Replace Temp with Query, Split Temporary Variable, and Replace Method with Method Object.

3. Moving Features Between Objects

Core concept: The key OO design decision is where responsibilities live. When Feature Envy, excessive coupling, or unbalanced class sizes show a method or field is in the wrong class, move it where it belongs.

Why it works: A method placed away from the data it uses creates invisible cross-class dependencies, so one logical change ripples across many files — Shotgun Surgery. Co-locating method and data confines the change to one class.

Key insights:

• Move Method when a method uses more of another class's features than its own; Move Field likewise
• Extract Class when one class does two things — split along the axis of change; Inline Class when one does too little
• Hide Delegate enforces the Law of Demeter; Remove Middle Man undoes it when forwarding becomes the whole class
• Resolve that tension case by case: hide the delegate when the chain is unstable, remove the middle man when it's pure forwarding

Code applications:

Context	Pattern	Example
Method envies another class	Move Method	calculateShipping() from Order to ShippingPolicy
God class 500+ lines	Extract Class	Pull Address fields/methods into own class
Client calls a.getB().getC()	Hide Delegate	Add a.getCThroughB()
Class only forwards calls	Remove Middle Man	Let client call the delegate directly

See references/moving-features.md when deciding where a responsibility belongs — mechanics for Move Method/Field, Extract/Inline Class, Hide Delegate, and Remove Middle Man.

4. Organizing Data

Core concept: Raw data — magic numbers, exposed fields, integer type codes — creates subtle bugs and scatters domain knowledge. Replace primitives with objects that encapsulate behavior and enforce invariants.

Why it works: An int amount has no rounding rules or currency code; a Money object encapsulates all of it, so business rules live in one place and the type system catches errors at compile time.

Key insights:

• Replace Magic Number with Symbolic Constant — the simplest data refactoring; it names intent
• Replace Data Value with Object cures Primitive Obsession (EmailAddress, Money, Temperature)
• Encapsulate Field and Encapsulate Collection — never expose raw fields or mutable internal lists
• Replace Type Code with Subclasses when the code affects behavior; with Strategy when subclassing is impractical
• Change Value to Reference when you need identity semantics (one shared Customer, not copies)

Code applications:

Context	Pattern	Example
if (status == 2)	Replace Magic Number	if (status == ORDER_SHIPPED)
String email passed everywhere	Replace Data Value with Object	EmailAddress class with validation
Getter returns mutable list	Encapsulate Collection	Return Collections.unmodifiableList(items)
int typeCode with switch	Replace Type Code with Subclasses	Employee → Engineer, Manager

See references/organizing-data.md when replacing primitives with objects — mechanics for Replace Data Value with Object, Change Value to Reference, Replace Magic Number, Encapsulate Field/Collection, and the Replace Type Code variants.

5. Simplifying Conditional Logic

Core concept: Deeply nested if/else trees, long switches, and scattered null checks are the hardest code to read and the most bug-prone. Named refactorings decompose, consolidate, and replace conditionals with clearer structures.

Why it works: A six-branch conditional forces readers to simulate every path mentally; well-named extracted branches are self-documenting, and polymorphism eliminates whole categories of "forgot this case" bugs.

Key insights:

• Decompose Conditional: extract condition, then-branch, and else-branch into named methods
• Consolidate Conditional Expression: merge conditions with the same result into one named check
• Replace Nested Conditional with Guard Clauses: handle edge cases early and return, keeping the main path unindented
• Replace Conditional with Polymorphism is the gold standard for type-based conditionals
• Introduce Special Case (Null Object) eliminates scattered if (x == null) checks; Introduce Assertion makes assumptions fail fast

Code applications:

Context	Pattern	Example
Long if with complex condition	Decompose Conditional	Extract isSummer(date) and summerCharge()
Deeply nested if/else	Replace with Guard Clauses	Edge cases first, return early, flat main path
Switch on object type	Replace Conditional with Polymorphism	Each type implements its own calculatePay()
if (customer == null) everywhere	Introduce Special Case	NullCustomer with safe default behavior

See references/simplifying-conditionals.md when untangling branches — before/after examples for Decompose/Consolidate Conditional, Guard Clauses, Replace Conditional with Polymorphism, Special Case, and Assertions.

6. Safe Refactoring Workflow

Core concept: Refactoring is only safe when wrapped in tests. The workflow is mechanical: run tests (green), apply one small transformation, run tests (green), commit. If tests go red, revert — don't debug a broken refactoring.

Why it works: Small steps make the failure obvious (it was the last thing you did) and reverting costs seconds; debugging a failed big-bang rewrite costs days.

Key insights:

• Rule of Three: tolerate duplication once, note it twice, refactor on the third occurrence
• Preparatory refactoring: restructure to make the feature easy before adding it; comprehension and litter-pickup refactoring keep code improving as you read and touch it
• When NOT to refactor: rewriting is easier, no tests and adding them isn't feasible, or the code will be deleted soon
• Refactor for clarity first, then profile and optimize the measured bottleneck — clear code is easier to tune
• Branch by Abstraction and Parallel Change enable large refactorings in production without long-lived branches

Code applications:

Context	Pattern	Example
About to add a feature	Preparatory Refactoring	Clean the insertion point first
Third copy of same logic	Rule of Three	Extract shared logic now
Large API change in production	Branch by Abstraction	Add abstraction layer, migrate callers, remove old path
Renaming a widely-used method	Parallel Change	Add new, deprecate old, migrate, remove

See references/refactoring-workflow.md before a large or risky refactoring — the full green-to-green cycle, when (not) to refactor, performance, Branch by Abstraction, and Parallel Change.

Common Mistakes

Mistake	Why It Fails	Fix
Refactoring without tests	No safety net to detect behavior change	Write characterization tests first
Big-bang rewrite	Mixes structural and behavioral change; undebuggable	Smallest possible steps, tests after each
Refactoring while adding features	Two hats at once — neither change verifiable	Refactor first (commit), then add feature (commit)
Renaming without updating callers	Broken build or dead code	Use IDE rename; search all references
Extracting too many tiny methods	Indirection without clarity when names are poor	Each name must remove the need to read the body
Ignoring the smell catalog	Reinvents fixes instead of applying proven recipes	Learn named smells; each maps to refactorings
Refactoring doomed code	Polish on condemned code is waste	Check the code's lifespan justifies the investment
Optimizing while refactoring	Conflates clarity with performance	Clarity first, then profile, then optimize hot path

Quick Diagnostic

Question	If No	Action
Do tests pass before you start?	No safety net	Write or fix tests first — never refactor red
Can you name the smell you're fixing?	Refactoring by instinct, not catalog	Identify the smell, apply its prescribed refactoring
Is each method under ~10 lines?	Long Methods likely	Extract Method into named steps
Does each class have one reason to change?	Divergent Change or Large Class	Extract Class to separate responsibilities
Are there duplicated code blocks?	The most expensive smell	Extract shared logic into common method/base class
Do conditionals use polymorphism where apt?	Switch Statements remain	Replace Conditional with Polymorphism
Are you committing after each step?	Risk losing work, mixing changes	Commit after every green-to-green transformation
Is the code easier to read after your change?	Refactoring added complexity	Revert and try a different approach

Further Reading

The definitive guides to improving existing code:

• "Refactoring: Improving the Design of Existing Code (2nd Edition)" by Martin Fowler
• "Working Effectively with Legacy Code" by Michael Feathers (companion for code without tests)
• "Clean Code: A Handbook of Agile Software Craftsmanship" by Robert C. Martin (complementary naming and style principles)

About the Author

Martin Fowler is Chief Scientist at Thoughtworks, a signatory of the Agile Manifesto, and author of Refactoring: Improving the Design of Existing Code (1999; 2nd edition 2018), which introduced catalog-based, named refactorings to mainstream development. His catalog underpins the automated refactoring tools in every major IDE.