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1. Rediscovering Simplicity

Design choices have cost, it makes sense to pay this cost if you also accrue benefits. Design is about picking right abstractions. But abstractions are hard. Overanticipating, premature abstractions => difficult situations. You should resist abstractions until they absolutely insist on being created. This book is about finding the right abstraction.

1.1 Simplifying Code

Contradictory Goals: Code must remain concrete enough to be understood and be abstract enough to allow for change. There’s a sweet spot that represents the perfect compromise between comprehension and changeability, and it’s your job to find it.

1.1.1. Incomprehensibly Concise

First solution: hard to understand because it is inconsistent and duplicative, contains hidden concepts w/o names.

Method versus Message

A method is defined by an object and contains behavior. A message is sent by an object to invoke behavior.

Questions to help you assess value and cost of the bit of code:

  • How difficult was it to write?
  • How hard is it to understand?
  • How expensive will it be to change?

1.1.2. Speculatively General

Difficult to write, too many levels of indirection.

Questions to ask:

  • how many verse variants are there?
  • which verses are most alike and in what way?
  • which verses are most different and in what way?
  • rule to determine what verse is sung next?

Value/cost questions:

  • How difficult was it to write?
  • How hard is it to understand?
  • How expensive will it be to change?

1.1.3. Concretely Abstract

Too many individual methods, several classes. Code is difficult to write. Methods are is easy to understand, but it's hard to get the point of entire song. A small change can lead to need to do many changes.

Small methods + DRYness should lead to code that is easy to change. But there wrong abstractions (name, level) lead to high cost of change.

1.1.4. Shameless Green

Easy to write, understand and change. But disturbing, cause it's not DRY.

If you DRY out duplication or create methods to extract code, you'll create levels of indirection and make code more abstract. In theory it makes code easier to understand and change, but in practice it often achieves th opposite.

1.2. Judging Code

We constantly judge code and make choices. But defining what code is good code is not an easy thing.

1.2.1. Evaluating Code Based on Opinion

There are lots of definitions of good code. Good code not only works, but is also simple, understandable, expressive and changeable. These definitions tell what good code is like, but don't tell how to achieve this state. None of these definitions is precise in a measurable way.

1.2.2. Evaluating Code Based on Facts

Code metrics:

Source Lines of Code

Cyclomatic Complexity

Counts the number of unique execution paths through a body of source code (many nested conditionals => high score, no nested conditionals => 0) Can be used to compare code, to determine overall complexity, to find out if you have enough tests. Easy and useful, but views the world through a narrow lens - code does more than just evaluate conditions.

Assignments, Branches and Conditions (ABC) Metric

  • variable assignments
  • function calls and message sent
  • conditional logic Measure of complexity. Ruby tool 'Flog'.

1.2.3. Comparing Solutions

Shameless Green has the lowest total Flog score, the second lowest SLOC, and the second lowest Worst Bit score. If your goal is to write straightforward code, these metrics point you toward Shameless Green.

1.3. Summary

As programmers grow they become comfortable with complexity. But experience programmers write simple code. Writing Shameless Green is fast, and the resulting code might be "good enough". It's duplicative and not very object-oriented, but if nothing ever changes the best strategy is to deploy this code and walk away. But if the change is required, the code may be not so good enough.

Test Driving Shameless Green

Investigation on how to create tests that lead to Shameless Green.

2.1. Understanding tests

TDD works like this:

  1. Write a test (red - failing)
  2. Make it run (green)
  3. Make it right (refactor)

Red/Green/Refactor cycle, the TDD mantra Get to green quickly, it means safety and simplifies all your following actions. This chapter concentrates on creating the tests and writing simple code to pass them. Future chapters refactor the resulting code to improve the design.

2.2. Writing the First Test

The first test is the hardest to write. It's important to sketch out an overall plan before you write the first test. Sketch out a public Application Programming Interface. It may have methods: verse(n), verses(a, b), song

It makes sense to begin by testing a single verse. TDD tells you to write the simplest code that will pass this test. So we write just enough code to change the error message.

class Bottles

Next steps:

  • add an empty method 'verse'.
  • add an argument (_)
  • add exact verse text to the method

Obviously, the second test will break the code. It may seem pointless to write transitional code, but it's the essence of TDD. TDD is toggling between wearing 2 hats:

  1. Writing tests hat - big picture, work with the overall plan in mind
  2. Writing Tests hat - pretend to know nothing other than the requirements, specified by the test.

2.3. Removing Duplication

Next test: you should do the simplest, most useful thing that proves that your code is incorrect. It's verse 3 (verses 3-99 are nearly identical, testing high and low ends) As the tests get more specific, the code get more generic.

2.4. Understanding Transformations

In the article Transformation Priority Premise there is a list of transformations that change code from more specific to more generic in priority order, from simpler to more complex.

2.5. Tolerating Duplication

Verses 0,1,2 are unique. It makes sense to test the verse 2.

  • write test
  • failure
  • possibilities: *new conditional:
if number == 2

*interpolated string:

"#{number-1} bottle#{'s' unless  (number-1)  ==  1}  of  beer"+
                           "on the wall.\n"

Choice by metrics: interpolated string, but metrics are wrong - can't find 'unless' conditional.

You could also think of #pluralization method. But it's not a correct abstraction - bottle/bottles is duplicated many times, it represents an important concept, but that's not a pluralization. You can't yet determine the right abstraction. So you have to choose: try to guess abstraction or tolerate duplication for a while. These questions may help you:

  • Does the change make code harder to understand?
  • What is the future cost of doing nothing now?
  • Will I soon get more information?

For now we'll resist creating an abstraction, cause the 'interpolated string' solution is harder to understand.

2.6 Hewing to the plan

So it's sometimes better to remove the duplication and sometimes to keep it. Think of reaching green quickly, create only unambigious abstractions, refactor later. Next step: test verse 1. It's possible to pass the test with interpolated logic, but according to the previous example it's better not to do so. Adding a case statement. Next step: test verse 0. We won't remove duplication currently - the cost of reaching green by adding one more branch is very low.

Now we have tests for all verse variants and code to make all of them pass. The code is easy to understand, because it doesn't have many levels of indirection.

2.7. Exposing Responsibilities

Creating and testing method verses(a, b) Creating a test for verses 88 and 89. Duplication is useful when it supplies independent, specific examples of a general concept that you don’t yet understand. But here it's not useful - it would duplicate already existing example.

def verses(_,_)

There are different ways to make tests pass:

  • Fake It (till you make it)
  • Obvious Implementation
  • Triangulate

'Obvious' idea is attractive, but it's also perilous. Sometimes obvious idea turns out to be the wrong guess. Fake It style may seem awkward, but with practice it becomes natural and speedy. It forces you to write better tests and is the antidote to hubris of thinking you know what's right. Obvious is for very small leaps.

Triangulate is "conservatively drive abstraction with tests" It means write several (breaking) tests at once and then one piece of code that will make all of them pass.

Let's test a few verses. And generalize the code. Choose between adding a conditional and making code more abstract. Here the best choice is abstraction.

2.8. Choosing Name

Creating song method. Let's reflect API on the whole. Does song serve it's purpose or is redundant and should be deleted? The song method imposes a single dependency; to use it, you need only know its name, but to use verses method the caller should know a lot.

2.9. Revealing Intentions

The #song is intention and verses(99, 0) is implementation. Song users don't need to know implementation details of verses.

2.10 Writing Cost-Effective Tests

TDD is not free, but benefits outweight the cost. But the promise of TDD is not universally fulfilled. Often tests are hard to understand and to changed, time-consuming. Sometimes test suites are even abandoned. Sometimes tests are tied too closely to code. The tests should confirm what your code does without knowing how.

2.11 Avoiding the Echo Chamber

Testing the song method: You may be tempted to test it using verses(99, 0) Asserting that the song returns the expected lyrics is very different from testing that the song returns the same as verses. This test would be too tighly coupled to the implementation. You also could have the wrong code and the test would still pass. The text needed for 100 verses is fairly lengthy, and you may resist writing out the full string because of concerns about duplication.

2.12 Considering Options

  1. Assert that the expected output matches that of some other method. Dependencies, that could break the song.
  2. Assert that the expected output matches a dynamically generated string. That would require logic, which can break the song in a confusing way.
  3. Assert that the expected output matches a hard-coded string. Only third idea is independent of the current implementation. DRY is good idea for code, but less useful in tests.

3. Unearthing Concepts

Shameless Green is simple and understandable, but it's also kind of procedural and contains duplication. It's good enough until the change is needed.

3.1. Listening to Change

The code is expensive, so improving code based only on aesthetic purposes may be not the best use of your precious time. Changes should be driven by requirements. A new requirement tells you how the code should be changed, changes impose higher standarts. New requirement: output '1 six-pack' in each place where it currently sais '6 bottles'. If we just add branches to case statement, it'll have 6 branches. This is unacceptable, so we should improve the code.

3.2. Starting With the Open/Closed Principle

Open/Closed - open for extension and closed for modification. 'O' in SOLID Single Responsibility - methods of the class should be cohesive around a single purpose Open/Closed - open for extension. closed for modification. Liskov Substitution - subclasses should be substitutable for their superclasses. Interface Segregation - objects should be forced to depend on methods they don't use Dependency Inversion - depend on abstractions, not concretions

Open/Closed - you should not conflate the process of refactoring with adding new features New requirement => first rearrange your code such that it'll be open to the new feature, then add the new code. Our code in not open for the new requirement, so we should refactor it. But it's hard to figure out how to do it. If you don't know how to make the code open, remove the easiest to fix/best understood code smell.

3.3. Recognizing Code Smells

The easiest way to unearth the code smells is to make a list of things you dislike about the code.

3.4. Identifying the Best Point of Attack

Our code contains the switch statement smell and duplicated code. Duplicated Code is the easiest. Removing duplication is not directly connected with the new requirement. The plan is to nibble away, one code smell at a time, in faith that the path to openness will be revealed.

3.5. Refactoring Systematically

Safe refactoring relies upon tests. If tests fail because you’ve broken the code, undo the last change, make a better one and proceed. If you rearrange code without changing behaviour but tests fail, the tests are flawed (e.g. tests know about implementation). In this situation you should improve the tests first.

3.6. Following the Flocking Rules

Current task - removing the duplication.

Flocking Rules

  1. Select the things that are most alike.
  2. Find the smallest difference between them.
  3. Make the simplest change that will remove that difference.

Steps for code change:

  • parse the new code
  • parse and execute it
  • parse, execute and use the result
  • delete unused code

With these steps you'll get very precise error messages when something goes wrong. As you gain experience you'll take larger steps.

For now:

  • change only 1 line at a time
  • run the tests after every change
  • if the tests fail undo and make a better change

Birds flocking is done by 3 simple rules:

  1. Alignment - Steer towards the average heading of neighbors
  2. Separation - Don’t get too close to a neighbor
  3. Cohesion - Steer towards the average position of the flock

3.7. Converging on Abstractions

The Flocking rules are atomic and general, they don't yet inspire confidence.

3.7.1 Focusing on Difference

When examining complicated problems, the eye is first drawn towards sameness. Sameness is easier to identify, but difference is more useful, cause it has more meaning. DRYing the difference has more value. The difference represents an abstraction. You don't have to identify abstraction in advance, just follow the rules and the abstraction will appear.

Programmers usually decide a solution and implement it, the next code may be surprising. Solution crafted by intention (usual way) => taking many small, iterative steps, resulting in solution, discovered by refactoring.

Back to the code: 2 case and else case are most alike, the only real difference is the word "bottle" versus the word "bottles."

3.7.2. Simplifying Hard Problems

Now we need to make the most alike strings identical. It's important to focus on this specific goal, go by horizontal path. The most difficult parts are the most interesting, but sometimes solving easy problems first transmute hard problems into easy ones. Next step: replace hard-coded numbers. Now the only difference is bottle vs bottles.

3.7.3. Naming Concepts

It's time to decide what bottle/bottles mean in the context of the song. The name of the thing should be one level of abstraction higher than the thing itself. 'Unit' is too general, not in the language of the domain. It may be useful to think of other things that would also be in the same category. E.g. the container, which is the good name.

3.7.4. Making Methodical Transformations

It's tempting to make all changes in one step. But making a slew of simultaneous changes is not refactoring — it’s rehacktoring. In real world you may end up with the ocean of red, then try to fix them, discard all of them and start anew. It's better to make a series of tiny changes and run tests after each. If tests fail, you'll know the exact change that caused a failure. If the tests pass, the code still works, even when the refactoring in partially complete.

The steps guided by flocking rules:

  • create an empty container method.
  • container returns 'bottles'
  • calling container in the else branch

Container should eventually receive an argument, but if we just add it, there will be ArgumentError: wrong number of arguments. Real applications can have similar probles but with 10-100-1000 callers instead of one.

3.7.5. Refactoring Gradually

A strategy not to edit all callers simultaniously: Container With Defaulted Argument:

def container(number=:FIXME)
  if number == 1

Once the refactor is complete, the default should be removed. Note, that this change is multiline, but it could be represented in single a line form. For now we'll keep it this way. Next steps:

  • really pass the argument in the else branch
  • pass the argument in the 2 branch
  • remove FIXME
  • remove the 2 branch - now 2 and else cases are identical

We've done 15 steps, 12 of them required changes in code. This way may seem very slow, but with practice it becomes very fast. The time is also recouped by avoiding lengthy debugging sessions. You can later combine steps if circumstances allow.

4. Practicing Horizontal Refactoring

Let's iteratively apply the Flocking Rules to get a more abstract sample to produce any verse.

4.1. Replacing Difference With Sameness

Next: the 1 and else cases are most alike.

Modifying first and second lines of these 2 branches:

  • replacing hard-coded 1 with number
  • bottle vs bottles => container(number)

4.2. Equivocating About Names

Third line: difference 'it' vs 'one'. Finding the good name for such fuzzy concept is hard. 'thing' is broad and 'it_or_one' is too narrow. Let's choose name 'pronoun' by now. Steps:

  • create empty pronoun method
  • alter pronoun to return 'one'
  • replace 'one' with pronoun
  • add defaulted value to pronoun (FIXME)
  • add conditional
  • pass the argument to pronoun
  • alter the pronoun calls to use argument
  • remove default value

4.3. Deriving Names From Responsibilities

Next difference: last line in 1 and else cases: 'no more bottles' vs "#{number-1} #{container(number-1)}"

  • replace 'bottles' with container(number-1)
  • 'no more' vs #{number-1}

We code name this concept 'remainder', but the 0 case also starts with 'no more', so we'll choose 'quantity'.

But that's not a general licence to think ahead at such points. When creating the abstraction, think of your current goal. First choose the responsibility, then search for a good name which reflects this responsibility.

4.4. Choosing Meaningful Defaults

:FIXME default is helpful, but won't work in every case.

  • creating quantity method, returning 'no more'
  • calling method in 1 case
  • add conditional to the quantity method (number == 0 ? 'no_more' : number) => tests fail (FIXME bottles of beer on the wall) FIXME default works only when you want to execute the false branch, for the true branch we need a more specific default.
  • reverting to the prev step
  • adding 0 as default
  • pass number-1 as an argument in the 1 branch
  • use quantity in the else case
  • the default can be removed
  • send container instead of bottles in 1 branch The 1 branch can be deleted.

4.5. Seeking Stable Landing Points

3 new concepts (quantity, pronoun, container) were defined. Each of them has a single responsibility, are of identical shape. It's the result of following specific rules. If you follow the rules of refactoring, you'll quickly arrive at stable points. The consistency enables the next refactoring.

4.6. Obeying the Liskov Substitution Principle

We have 2 remaining branches. Let's find the next smallest difference. It's 'No more' vs number. It looks like the quantity concept, but it would return the lowercase 'no more' and broke the tests. So let's replace 'no more' with quantity(number).capitalize in the 0 case If we replace the same thing in the else branch, the tests will fail with NoMethodError. Review the quantity method: the true branch returns a String and the false branch returns Integer. You may want to change the quantiry method, but let's firstS explicitly call to_s for both results of quantity method, that fixes the tests.

Liskov Substitution Principle

The official definition is 'the subtypes should be substitutable for their supertypes' But the LSP also applies to duck types - they should be substitutable for each other. Liskov prohibits you from doing anything that would force the sender of the message to test the result of this message in order to know how to behave. So we should modify the quantity method to always return string (to_s for the number) instead of sending to_s to the results. Next: replace bottles with container in the 0 case. Now the first lines of 2 branches are the same.

4.7. Taking Bigger Steps

It's beginning to feel that there's a common refactoring pattern. If this theory is corrent, it makes sense to combine several steps to speed up. Let's examine the second lines of the remaining branches. Replace the corresponding strings with the calls to quantity and container. Lines 1 and 2 are now identical. The next difference: lines 3 are very different, but they represent a single concept. We must name a concept, create a method to represent it and replace the strings with a message send. The concept is an action to take depending on the number of bottles. We can do it in smallest steps or combine them. If we take bigger steps, we'll create an action method. Then replace 3rd lines with action message send.

Combining steps now is different from previous container method creation, cause now you've practiced the Flocking Rules. Now it makes sense to take bigger steps. But if you combine steps and the tests fail, first return to green and make incremental changes.

4.8. Discovering Deeper Abstractions

There is only 1 difference left now - the 4th lines, let's resolve it. 'bottles' is the same as container, so we need to resolve '99' vs qunatity difference. We may want to alter quantity method and replace if with a case statement. But smth is deeply wrong eith this solution: -1 is invalid number of beers. Questions:

  • what is the responsibility of the quantity method?
  • is there a way to make 4-th phrases more alike or identical?

99 is not a special case of quantity concept. You can replace the lines more alike by replacing 99 with quantity(99) Replace 'bottles' with a call to container method. '99' must represent the same concept ad number-1. The concept should know that when the number is 50, result is 49 and when the number is 0, the result is 99. There are #succ and #pred methods in ruby (4.succ => 5, 'b'.pred => 'a', etc) In our case the main direction is down, expect the verse 0 case, which is followed by 99. The term successor is right, here it means following, not higher. First successor implementation:

def successor(number)
  number - 1


  • replace number-1 with successor in the else branch
  • make successor open to be used with 0, add if statement
  • replace 99 with successor

The 0 and else cases are now identical.

Successor is important and separating it from quantity gives both methods a single responsibility.

4.9. Depending on Abstractions

Abstractions are benefitial in many ways: consolidating code, so it becomes a shortcut for an idea, the code can be changed with ease. Another subtle benefit: they tell where code relies upon an idea. To get this benefit you must refer to an abstraction in every place where it applies. E.g. container(number-1). This code doesn't actually want the container of number-1, it wants the container for a following verse. You should replace all occurencies of number-1 with successor(number). One more refactoring trick to prove that this common template work for all changes: Add the whole verse text to the end of the method whithout removing case statements. Now we can remove the case statement. It's important to ask yourself if the new code is better. The Flog score is worse, but the code is better. The new concepts are revealed and isolated in the new code. If several programmers follow the Flocking Rules, their resulting code will be the same.

5. Separating Responsibilities

The impetus of our refactoring was the nex 'six-pack' requirement.

5.1. Selecting the Target Code Smell

Code should be opened for extension and closed for modification. Let's reexamine the current code and find if it's open for the new requirement. A code is not yet open for it. The truth about refactoring is that sometimes it makes things worse. Proper refactoring allowes you to explore a problem domain safely. We've done the refactoring, but the code is not yet open. Now we must decide whether it's better to proceed with additional modifications or revert to the previous state and try one more time.

The current code is not open but it's improved, so let's keep it. You must continue to be guided by code smells.

5.1.1. Identifying Patterns in Code

5.1.1. Spotting Common Qualities

5.1.3. Enumerating Flocked Method Commonalities

Questions to ask:

Do any methods have the same shape?

The Flocked Five methods are all of the same shape. It’s not yet clear what it means that these methods have the same shape, but it’s important to notice that they do.

Squint Test - one easy way to judge the code.
  • put the code of interest on the screen
  • lean back
  • squint your eyes, such as you can still see the code, but can no longer read it or zoom out.
  • look for the changes in shape and in color Changes of shape reveal the presence of conditionals. Two or more levels of indentation result in multiple execution paths and add complexity. Changes of color indicate differences in the level of abstraction. A method that mixes many colors tells a story that may be difficult to follow.

Do any methods have the arguments of same shape?

6 methods take the same argument - the Flocking Five and the verse method.

Do arguments of the same name always mean the same thing?

Verse method - number represents the verse number. Number in the Flocking Five method is a bottle number. These methods (verse vs five) use the same argument name to represent different concepts. This is rarely a good idea. The naming mistakes make it harder to notice underlying code smells, now you must examine the arguments and clarify the abstractions they represent (verse number - for the verse, bottle number - for the flocking five methods)

Where would you put the private keyword in this class?

Before the flocking five

How would you break the class into 2 pieces?


For the Flocking Five methods (container, quantity, action, pronoun and successor):

Do the tests in the conditionals have anything in common?

Thy all test that number is exactly equal to another value.

How many branches do conditionals have?


Do the methods contain any code rather than the conditionals?


Do methods that take number as an argument depend more on number or more on a class as a whole?

The flocked five depend only on the number argument rather than on the rest of the class.

5.1.4. Insisting Upon Messages

The flocking five method are flawed from the point of view of the OO practitioner. This solution is optimized to understandability instead of changeability. The above pattern means that objects are missing.

If you see the conditional, be suspicious. Conditionals are useful in OOP apps. Manageable OO applications consist of pools of small objects that collaborate to accomplish tasks. E.g. some object, somewhere, must choose which objects to create, and this often requires conditionals.

There’s a big difference between a conditional that selects the correct object and one that supplies behavior. The first is acceptable and unavoidable. The second suggests that you are missing objects in your domain.

To preserve code ignorance, minimizing dependencies is required. The container methods yearns to be injected with a smarter object to which it could forward the message:

def container

5.2. Extracting Classes

A number of methods take the same argument, have the same shape, contain a conditional, could be private and depend more on argument than on the class as a whole. These are traits that point on a Primitive Obsession code smell. Built-in data classes like String, Integer, Array , and Hash are examples of "primitives." The cure for Primitive Obsession is to create a new class to use in place of the primitive. The refactoring recipe is Extract Class.

5.2.1. Modeling Abstractions

You must now choose a name for a new class. The primitive you are replacing represents a bottle number. Not a bottle (physical), but a bottle number (idea). The power of OO is that you create a virtual world, in which ideas (e.g. Purchase, Discount, etc) are real. Don't focus on physical things only when designing an app.

Name of our class: there are two most obvious choices - ContainerNumber and BottleNumber.

5.2.2. Naming Classes

The rule is: name methods one level of abstraction higher than current implementation. If we extrapolate this rule to classes, the name should be ContainerNumber. But the name BottleNumber is good enough. BottleNumber - less flexible, straightforward. ContainerNumber - more abstract. That rule applies more to numbers than to classes. The classes must be named after the name they are, you can revisit this desicion if things change later.

5.2.3. Extracting BottleNumber

This chapter extracts a new class BottleNumber without using TDD but following a modified Martin Fowler Extract File refactoring recipe. The Bottles tests will be a safety net for the new class. They'll be a kind of integration tests (indirectly test BottleNumber).

Four steps will stull apply:

  • parse the new code
  • parse and execute
  • parse, execute and use its result
  • delete unused code

First step: create an empty BottleNumber class. Next: copy the flocking five to the BottleNumber.

Running the tests at this point just parses the code and checks that it's syntactically correct. Official 'Extract Class' recipe: linking the old class to the new, move attributes, then methods of interest. We'll combine all of the steps moves in a single change. That's a large leap, but you can be confident, the code is consistent.

Add attr_reader :number to the BottleNumber and an initialize method Next step is to execute a bit of the new code without using the result: Add:

to the beginning of the container method of the Bottles class. The code is ugly - it requires passing the number argument twice, we'll improve it later. Next small step: use the result of the new method, move the code from the beginning to the end of the Bottles container method. Now you can delete the old implementation. Repeat the above procedure for each of the methods.

5.2.4. Removing Arguments

You could just remove the argument and modify the caller, but that would be a multiline change. In real world application the method is called in many places, so that wouldn't be a simple change. Let's rename the argument and add the default:

def container(delete_me=nil)

Now the argument is optional and you can remove the redundant argument from the caller. After the call is modified you can remove delete_me argument. The steps:

  • change argument to delete_me = nil arg
  • change every sender to remove the parameter
  • delete the argument from the method definition

5.2.5. Trusting the Process

Removing the argument by these steps work with action, quantity and action. But for pronoun tests begin to fail. There is a call to pronoun in the action method. In the step 2 we should remove the number argument from the action method. Once you do this and follow all steps, the tests will pass.

If tests begin to fail during refactoring, take a closer look at the code.

5.3. Appreciating Immutability

To mutate means to change. In physical world conditions vary over time. Most OO programmers write code that expects and relies upon object mutation. That feels natural, but mutation is not an absolute requirement. It's possible to build apps creating only immutable objects. Concerns: performance. Benefits:

  • easy to understand and reason about, won't secretly morph into something else.
  • thread safe

5.4. Assuming Fast Enough

The benefits of immutability are so great that if it was free, you would choose it every time. You should become reconciled to the idea and you may require creation of many new objects. It's time to discuss caching. Caching is easy, but finding out when cache should be updated can be hard. Costs of caching and mutation are interrelated: if a thing doesn't mutate, the local copy is good forever. If the thing changes, you must write additional code to recognize if your copy is stale. Outdated cache can be a source of frustrating bugs. Mutation and caching complicate code, sometimes complication is required for performance improvement, sometimes not. The best strategy is to write the simplest code and measure its performance. The goal is optimize for ease of understanding while maintaining acceptable performance.

5.5. Creating BottleNumbers

Even if you are comfortable with object creation, the code construct a large amount of BottleNumbers. 900 instances are created for 100 verses. That feels excessive. All of the methods creating BottleNumbers are called from the verse method. We can cache the instance of BottleNumber for the whole verse or some lines of it, or even for the whole song, but it's not free.


  • create a variable in the beginning of the verse (that's a temporary variable code smell)
bottle_number =

The complexity is not raised much, the benefits outweight the costs.

  • gradually alter the verse template to send messages to the new object

Now only the phrase four (with successors) remains to be updated.

5.6. Recognizing Liskov Violations

The goal is to replace


with something like


The problem is that successor returns the number, though it should logically return a BottleNumber. This inconsistency is another violation of Liskov Substition Principle. It's better to finish horizontal refactoring for now before fixing the Liskov violation.

For now you can create another temp variable next_bottle_number and use it in the last phrase.

next_bottle_number =

The code still exudes code smells (duplication, conditionals, temp fields, etc) and violates LSP. But this code is consistent and regular, embodies a stable point and enables the next refactoring.

6. Acheiving Openness

The code is still not open for six-pack refactoring.

6.1. Consolidating Data Clumps

The easiest code smell. quantity and container appear together in 3 different places. Having a clump of data usually means that you are missing a concept. Full-grown Data Clumps are usually removed by extracting a class. But for this small example it makes sense to simply create a new method.

All ruby objects know #to_s method. It's acceptable to override the default behavior. You can implement #to_s method in BottleNumber (wich returns quantity and container). The you can replace each occurence of quantity+container with just #{bottle_number}.

Using to_s to remove this data clump reduces the amount of code, but comes close to abusing the intent of to_s. This to_s implementation is specific and may be ill-suited for use in other situations.

In real life you need a more general implementation of to_s, but for now current implementation provides a great illustration of the value of clump removal

The method is still not perfect, but removing the data clump improved readability and made its intentions more clear.

6.2. Making Sense of Conditionals

Next code smell: many conditionals of the same shape.

Fowler offers several refactoring recipes:

  • replace conditional with state/strategy Dispersing their branches into new smaller objects one of which is selected later and plugged back in at runtime. Using compositon.
  • replace conditional with polymorphism Creating one class to hold the defaults of the conditionals (the false branches) and adding subclasses for each specialization (the true branches of the various conditions). It then chooses one of these new objects to plug back at runtime. Uses inheritance.

Replace Conditionals with Polymorphism leads to the code arragement which is good for the six-pack requirement. Skilled programmers are good at picking code smells- that's result of a lifetime of coding experiments. But sometimes they also need careful, precise and reversible conding experiments.Polymorphism refers to the idea Practice builds intuition. Do it enough, and you’ll seem magical too.

6.3. Replacing Conditionals with Polymorphism

Polymorphism refers to the idea of having many different kinds of objects that respond to the same method. It allows senders to depend on the message while remaining ignorant of the type, class of the receiver. Senders don't care whare receivers are, but care what receivers do.

6.3.1. Dismembering Conditionals

BottleNumber's methods share a common shape, and they all contain checks for 0 and 1. That means that 0 and 1 are special and need to be smarter. Primitive Obsessions are usually cured by extracting a class.

Each conditional provides specific behavior in the true branch and generalized behavior in its false.

For the transition you'll need to create separate classes for the logic of 0 and the logic of 1, and some additional code to choose the correct class based on the value of number.

To begin choose one of the values: 0. Next, decide the name of the class: BottleNumber0. Create BottleNumber0 as an empty subclass of BottleNumber.

Modern OOP is biased towards inheritance, but that doesn't mean that inheritance is banned.

  • copy(not cut) one of the methods from BottleNumber to BottleNumber0, e.g. quantity.
  • remove the part of the BottleNumber0 method, that isn't about 0

Current verse implemetation is tightly coupled to the BottleNumber class. One way to find out if you need BottleNumber or BottleNumber0 is like this:

bottle_number = (number == 0 ? BottleNumber0 : BottleNumber).new(number)

It works, but is not optimal.

6.3.2. Manufacturing Objects

When several classes play a common role, some code, somewhere, must know how to choose the right role-playing class. This choosing very often involves a conditional, which should exist in one and only one place. Code like this 'manufactires' an instance of the right kind of object and is often referred as a factory.

The factory's sole responsibility is creating objects to play the role, to isolate the names of specific classes, to hide the logic needed to choose the correct one.

Now you need a bottle number factory. The first step is to isolate the creation of BottleNumbers in a single method of Bottles.

def bottle_number_for(number)

Next - replace in the verse method with bottle_number_for.

Change the bottle_number_for to return BottleNumber0 or BottleNumber depending on the number. Now you can remove everything but the default (false) branch for BottleNumber's quantity method.

The next goal was to reduce the subclass' conditional to its true branch, and the superclass' to its false.

BottleNumber and BottleNumber0 classes are substitutable for each other. To use the factory you don't need to know the class of the resulting object.

6.3.3. Prevailing with Polymorphism

Recipe's steps:

  1. Create a subclass to stand in for the value upon which you switch. a) Copy one method that switches on that value into the subclass. b) In the subclass, remove everything but the true branch of the conditional. i) Create a factory if it does not yet exist, and ii) Add this subclass to the factory if not yet included. c) In the superclass, remove everything but the false branch of the conditional. d) Repeat steps a-c until all methods that switch on the value are dispersed.
  2. Iterate until a subclass exists for every different value upon which you switch.

Follow those steps for action and successor methods.

The next step is repeating the entire procedure for 1. Then choose a method that is obsessed by 1 and copy it to the subclass. E.g. container method. As currently written the factory must be updated every time the new bottle number class gets created. For now you need to add a case statement to the factory method.

Add it and continue for all methods, that obsess on 1.

Despite the problem with the successor, the overall code fairly accurately reflects the domain.

6.3.4. Making Peace With Conditionals

The current factory contains a conditional which is similar to the Shameless Green conditional. But the Shameless Green had a branch for the verse #2, the current solution does not. Verse 2 is special, but bottle number 2 is not. The Shameless Green knows exactly what to do, the factory does not - it know how to choose who does. We've moved from procedural to OO code. The hard truth is that you can't avoid conditionals. But you can confine conditionals to factories and use polymorphism to create a pluggable behavior.

6.4. Transitioning Between Types

The successor method violates the Liskov Substitution Principle. It may cause increasing harm, each place where you call successor will have to know that it returns an Integer. When it was first created, there was no violation, it appeared when we transfered successor to the BottleNumber class. Now the problem is even worse, cause there are 2 successor methods: in BottleNumber0 and BottleNumber1. Needed alterations:

  • the factory should be reachable by the successor method
  • the successor method should invoke the factory
  • the verse method should expect successor to return a bottle number

It's reasonable to put the factory into the BottleNumber class and simplify the method name, bottle_number_for would be redundant.

class BottleNumber
  def self.for(number)

For now the factory will have to handle both types (Integer and BottleNumber) cause you can't modify all callers at one time:

def self.for(number)
  return number if number.kind_of(BottleNumber)

Replace bottle_number_for in the verse method with BottleNumber.for . bottle_number_for in Bottles is obsolete and can be deleted. Next: change the successor method in BottleNumber0:

def successor

same for BottleNumber#successor.

Now we want to replace next_bottle_number initialization in #verse with:

next_bottle_number = bottle_number.successor

For now, add this code after the previous initialization. Tests pass, you can confidently delete the old line.

Now the variable next_bottle_number is used only in one place. Temporary variables used just once may be removed by Inline Temp refactoring.

The guard clause in the factory is obsolete and can be removed.

Correcting the Liskov violation is important because OO programming relies on explicit trust and implicit contracts between objects.

Trustworthy objects are joy to work with, cause they behave as you expect. If you can't trust objects, they need to know too much about each other, it usually leads to lots of conditionals and type checking.

6.5 Making the Easy Change

You can now create a class for bottle number #6, that will return a six-pack. It's time to switch back to TDD mode. The factory is not open and must be updated to handle BottleNumber6. Steps:

  • change the test to print '1 six-pack' when needed
  • create a class BottleNumber6 with the corresponding container method
  • add a 6-branch to a case statement in the factory
  • create the BottleNumber6#quantity method

Make the change easy (warning: this may be hard), then make the easy change

Kent Beck Most of this book has been concerned with making the change easy.

6.6. Defending the Domain

Instead of implementing quantity and container for BottleNumber6 we could just override the #to_s method.

def to_s
  '1 six-pack'

It's shorter, but not better. Quantity and container represent reflect the fundamental concepts in this domain. BottleNumbers are now independent objects and can be used in other contexts. Overriding #to_s directly corrupts the BottleNumber6 with the knowledge of the inner Bottles verse template. This implementation couples BottleNumber6 to the context.

6.7. Prying Open the Factory

There are 3 conditionals in the factory method. Let's see if it's possible to create a factory open for an extension. Using metaprogramming to create an Open Factory:

class BottleNumber
  def self.for(number)
    rescue NameError

The factory is open, but there are some disadvantages:

  • the code is harder to understand
  • BottleNumber0, etc classes are not explicitly referenced in the source code E.g. the code will be in danger of deleting during the cleanup zeal
  • the code uses an exception for flow control
  • the factory ignores bottle number classes whose names do not follow the convention

It depends what option to choose: it depends. If you create new bottle number classes often, the cost of changing the factory method may be bigger than the cost of making it open. If you never create new bottle classes, there is not justification for complicating the code. A factory’s fundamental job is to manufacture the correct player of a role and it's openness can be tweaked over time.


Goals of the book:

  1. Process: supply you techniqies to improve your code: Strive for simplicity. Don’t abstract too soon. Focus on smells. Concentrate on difference. Take small steps. Follow the Flocking Rules. Refactor under green. Fix the easy problems first. Work horizontally. Seek stable landing points. Be disciplined. Don’t chase the shinything. New requirement: first refactor, then write the new code.

  2. The book wants you to fall in love with polymorphism.

The secret to programming happiness is to combine the canons with the infection, building applications from polymorphic, trustworthy objects, and changing them one step at a time.

Hold high standards, but judge yourself gently. Think of your code as a message in a bottle, written in haste for future readers. Your job is not to be perfect, but to write a generous and sympathetic story.