Clean Code

An introduction to clean coding best practices, based on R Martin's book Clean Code

This article is based mostly on R Martin's book "Clean Code". This book is a "prequel" to his next book "Agile Software Development: Principles, Patterns, and Practices(PPP)". The PPP book concerns itself with the principles of object-oriented design, and many of the practices used by professional developers.

I will succinctly present some of the main ideas presented in his book about writing better code as a programmer.

What is clean code

There are probably as many definitions as there are programmers. Clean code is something that every programmer should take care of. In the next sections we will discover some of the main attributes of well-written code.

1. Meaningful Names

Names are everywhere in software. We name our variables, our functions, our arguments, classes, and packages. We name our source files and the directories that contain them. Choosing good names takes time but saves more than it takes. So take care with your names and change them when you find better ones. Everyone who reads your code (including you) will be happier if you do.

1.1 Use Intention-Revealing Names

If a name requires a comment, then the name does not reveal its intent.

int d; // elapsed time in days

The name d reveals nothing. It does not evoke a sense of elapsed time, nor of days. We should choose a name that specifies what is being measured and the unit of that measurement, eg elapsedTimeInDays. Choosing names that reveal intent can make it much easier to understand and change code.

1.2 Avoid Disinformation

Do not refer to a grouping of accounts as an accountList unless it’s actually a List. The word list means something specific to programmers. If the container holding the accounts is not actually a List, it may lead to false conclusions. So accountGroup or bunchOfAccounts or just plain accounts would be better.

Beware of using names which vary in small ways. How long does it take to spot the subtle difference between a XYZControllerForEfficientHandlingOfStrings in one module and, somewhere a little more distant, XYZControllerForEfficientStorageOfStrings?

1.3 Make Meaningful Distinctions

Number-series naming (a1, a2, .. aN) is the opposite of intentional naming. Such names are not disinformative—they are noninformative; they provide no clue to the author’s intention.

Noise words are another meaningless distinction. Imagine that you have a Product class. If you have another called ProductInfo or ProductData, you have made the names different without making them mean anything different. Info and Data are indistinct noise words like a, an, and the.

Certainly a loop counter may be named i or j or k if its scope is very small and no other names can conflict with it.

1.4 Use Pronounceable Names

A significant part of our brains is dedicated to the concept of words. And words are, by definition, pronounceable. It would be a shame not to take advantage of that huge portion of our brains that has evolved to deal with spoken language. So make your names pronounceable.

1.5 Use Searchable Names

Single-letter names and numeric constants have a particular problem in that they are not
easy to locate across a body of text. The name e is a poor choice for any variable for which a programmer might need to search. It is the most common letter in the English language and likely to show up in every passage of text in every program. In this regard, longer names trump shorter names, and any searchable name trumps a constant in code.
My personal preference is that single-letter names can ONLY be used as local variables inside short methods.

1.6 Class Names

Classes and objects should have noun or noun phrase names like Customer, WikiPage, Account, and AddressParser. Avoid words like Manager, Processor, Data, or Info in the name of a class. A class name should not be a verb.

1.7 Method Names

Methods should have verb or verb phrase names like postPayment, deletePage, or save. Accessors, mutators, and predicates should be named for their value and prefixed with get, set, and is:

string name = employee.getName();
if (paycheck.isPosted())...

1.8 Pick One Word per Concept

Pick one word for one abstract concept and stick with it. For instance, it’s confusing to have fetch, retrieve, and get as equivalent methods of different classes.

Likewise, it’s confusing to have a controller and a manager and a driver in the same code base. What is the essential difference between a DeviceManager and a Protocol Controller? Why are both not controllers or both not managers? Are they both Drivers really? The name leads you to expect two objects that have very different type as well as having different classes.

1.9 Use Problem Domain Names

Separating solution and problem domain concepts is part of the job of a good programmer
and designer. The code that has more to do with problem domain concepts should have names drawn from the problem domain.

This is especially used in Domain Driven Design, where a great accent is put on the domain model. A common ubiquitos language is developed defining some domain names which are well understood for all the stakeholders.

The hardest thing about choosing good names is that it requires good descriptive skills and a shared cultural background. This is a teaching issue rather than a technical, business, or management issue. As a result many people in this field don’t learn to do it very well.

Follow some of these rules and see whether you don’t improve the readability of your code. If you are maintaining someone else’s code, use refactoring tools to help resolve these problems. It will pay off in the short term and continue to pay in the long run.

2. Functions

Functions are the first line of organization in any program. Writing them well is the topic of this chapter.

2.1 Small!

The first rule of functions is that they should be small.

2.2 Do One Thing

If a function does only those steps that are one level below the stated name of the function, then the function is doing one thing. After all, the reason we write functions is to decompose a larger concept (in other words, the name of the function) into a set of steps at the next level of abstraction.

Another way to know that a function is doing more than “one thing” is if you can extract another function from it with a name that is not merely a restatement of its implementation

2.3 Reading Code from Top to Bottom: The Stepdown Rule

We want the code to read like a top-down narrative. We want every function to be followed by those at the next level of abstraction so that we can read the program, descending one level of abstraction at a time as we read down the list of functions.

2.4 Switch Statements

It’s also hard to make a switch statement that does one thing. By their nature, switch statements always do N things. Unfortunately we can’t always avoid switch statements, but we can make sure that each switch statement is buried in a low-level class and is never repeated. We do this, of course, with polymorphism.

Consider the following example. It shows just one of the operations that might depend on the type of employee.

public Money calculatePay(Employee e)
throws InvalidEmployeeType {
switch (e.type) {
return calculateCommissionedPay(e);
case HOURLY:
return calculateHourlyPay(e);
return calculateSalariedPay(e);
throw new InvalidEmployeeType(e.type);

There are several problems with this function. First, it’s large, and when new employee types are added, it will grow. Second, it very clearly does more than one thing. Third, it violates the Single Responsibility Principle (SRP) because there is more than one reason for it to change. Fourth, it violates the Open Closed Principle (OCP) because it must change whenever new types are added.

The solution to this problem is to bury the switch statement in the basement of an ABSTRACT FACTORY,9 and never let anyone see it. The factory will use the switch statement to create appropriate instances of the derivatives of Employee, and the various functions, such as calculatePay:

public class EmployeeFactoryImpl implements EmployeeFactory {
public Employee makeEmployee(EmployeeRecord r) throws InvalidEmployeeType {
switch (r.type) {
return new CommissionedEmployee(r) ;
case HOURLY:
return new HourlyEmployee(r);

2.5 Use Descriptive Names

The smaller and more focused a function is, the easier it is to choose a descriptive name. Don’t be afraid to make a name long. A long descriptive name is better than a short enigmatic name. A long descriptive name is better than a long descriptive comment. Use a naming convention that allows multiple words to be easily read in the function names, and then make use of those multiple words to give the function a name that says what
it does.

Don’t be afraid to spend time choosing a name. Indeed, you should try several different names and read the code with each in place. Choosing descriptive names will clarify the design of the module in your mind and help you to improve it. It is not at all uncommon that hunting for a good name results in a
favorable restructuring of the code.

Be consistent in your names. Use the same phrases, nouns, and verbs in the function names you choose for your modules.

2.6 Function Arguments

The ideal number of arguments for a function is zero (niladic). Next comes one (monadic), followed closely by two (dyadic). Three arguments (triadic) should be avoided where possible. More than three (polyadic) requires very special justification—and then shouldn’t be used anyway.

When you are reading the story told by the module, includeSetupPage() is easier to understand than includeSetupPageInto(newPage- Content). The argument is at a different level of abstraction than the function name and forces you to know a detail (in other words, StringBuffer) that isn’t particularly important
at that point.

Arguments are even harder from a testing point of view. Imagine the difficulty of writing all the test cases to ensure that all the various combinations of arguments work properly. If there are no arguments, this is trivial. If there’s one argument, it’s not too hard. With two arguments the problem gets a bit more challenging. With more than two arguments, testing every combination of appropriate values can be daunting.

Output arguments are harder to understand than input arguments. When we read a function, we are used to the idea of information going in to the function through arguments and out through the return value. We don’t usually expect information to be going out through the arguments.

2.7 Monadic functions

Try to avoid any monadic functions that don’t follow these forms, for example, void includeSetupPageInto(StringBuffer pageText). Using an output argument instead of a return value for a transformation is confusing. If a function is going to transform its input argument, the transformation should appear as the return value.

2.8 Flag Arguments

Flag arguments are ugly. Passing a boolean into a function is a truly terrible practice. It immediately complicates the signature of the method, loudly proclaiming that this function does more than one thing. It does one thing if the flag is true and another if the flag is false!

2.9 Dyadic Functions

There are times, of course, where two arguments are appropriate. For example, Point p = new Point(0,0); is perfectly reasonable.

Dyads aren’t evil, and you will certainly have to write them. However, you should be aware that they come at a cost and should take advantage of what mechanims may be available to you to convert them into monads. For example, you might make the writeField method a member of outputStream so that you can say outputStream.writeField(name). Or you might make the outputStream a member variable of the current class so that you don’t have to pass it. Or you might extract a new class like FieldWriter that takes the outputStream in its constructor and has a write method.

2.10 Triads

Functions that take three arguments are significantly harder to understand than dyads. The issues of ordering, pausing, and ignoring are more than doubled. I suggest you think very carefully before creating a triad.

2.11 Argument Objects

When a function seems to need more than two or three arguments, it is likely that some of those arguments ought to be wrapped into a class of their own. Consider, for example, the difference between the two following declarations:

Circle makeCircle(double x, double y, double radius);
Circle makeCircle(Point center, double radius);

2.12 Verbs and Keywords

Choosing good names for a function can go a long way toward explaining the intent of the function and the order and intent of the arguments. In the case of a monad, the function and argument should form a very nice verb/noun pair. For example, write(name) is very evocative.

2.13 Have No Side Effects

Side effects are lies. Your function promises to do one thing, but it also does other hidden things. Sometimes it will make unexpected changes to the variables of its own class. Sometimes it will make them to the parameters passed into the function or to system globals. In either case they are devious and damaging mistruths that often result in strange temporal couplings and order dependencies.

2.14 Output Arguments

Arguments are most naturally interpreted as inputs to a function. In general output arguments should be avoided. If your function must change the state of something, have it change the state of its owning object.

2.15 Command Query Separation

Functions should either do something or answer something, but not both. Either your
function should change the state of an object, or it should return some information about
that object. Doing both often leads to confusion.

2.16 Prefer Exceptions to Returning Error Codes

Returning error codes from command functions is a subtle violation of command query separation. It promotes commands being used as expressions in the predicates of if statements.

if (deletePage(page) == E_OK)

If you use exceptions instead of returned error codes, then the error processing code can be separated from the happy path code and can be simplified:

try {
catch (Exception e) {

2.17 Extract Try/Catch Blocks

Try/catch blocks are ugly in their own right. They confuse the structure of the code and mix error processing with normal processing. So it is better to extract the bodies of the try and catch blocks out into functions of their own.

public void delete(Page page) {
try {
catch (Exception e) {
private void deletePageAndAllReferences(Page page) throws Exception {
private void logError(Exception e) {

2.18 Error Handling Is One Thing

Functions should do one thing. Error handing is one thing. Thus, a function that handles errors should do nothing else. This implies (as in the example above) that if the keyword try exists in a function, it should be the very first word in the function and that there should be nothing after the catch/finally blocks.

2.19 Structured Programming

Dijkstra said that every function, and every block within a function, should have one entry and one exit. Following these rules means that there should only be one return statement in a function, no break or continue statements in a loop, and never, ever, any goto statements.

While we are sympathetic to the goals and disciplines of structured programming, those rules serve little benefit when functions are very small. It is only in larger functions that such rules provide significant benefit.

So if you keep your functions small, then the occasional multiple return, break, or continue statement does no harm and can sometimes even be more expressive than the single- entry, single-exit rule. On the other hand, goto only makes sense in large functions, so it should be avoided.

2.20 How to write such "clean functions"?

When I write functions, they come out long and complicated. They have lots of indenting and nested loops. They have long argument lists. The names are arbitrary, and there is duplicated code. But I also have a suite of unit tests that cover every one of those clumsy lines of code.

So then I massage and refine that code, splitting out functions, changing names, eliminating duplication. I shrink the methods and reorder them. Sometimes I break out whole classes, all the while keeping the tests passing.


Nothing can clutter up a module more than frivolous dogmatic comments. Nothing can be quite so damaging as an old crufty comment that propagates lies and misinformation.

3.1 Comments Do Not Make Up for Bad Code

Clear and expressive code with few comments is far superior to cluttered and complex code with lots of comments. Rather than spend your time writing the comments that explain the mess you’ve made, spend it cleaning that mess./p>

3.2 Explain Yourself in Code

It takes only a few seconds of thought to explain most of your intent in code. In many cases it’s simply a matter of creating a function that says the same thing as the comment you want to write.

Instead of:

// Check to see if the employee is eligible for full benefits
if ((employee.flags & HOURLY_FLAG) &&
(employee.age > 65))

You could write:

if (employee.isEligibleForFullBenefits())

3.3 Good Comments

Some comments are necessary or beneficial. We’ll look at a few that I consider worthy of the bits they consume.

Sometimes our corporate coding standards force us to write certain comments for legal reasons. For example, copyright and authorship statements are necessary and reasonable things to put into a comment at the start of each source file.

Following is a list of "good" comments:

3.4 Informative Comments:

// Returns an instance of the Responder being tested.
protected abstract Responder responderInstance();

but it is better to use the name of the function to convey the information where possible. For example, in this case the comment could be made redundant by renaming the function: responderBeingTested.

For regular expressions we can add a comment to express it's intent:

// format matched kk:mm:ss EEE, MMM dd, yyyy
Pattern timeMatcher = Pattern.compile(
"\\d*:\\d*:\\d* \\w*, \\w* \\d*, \\d*");

3.5 Explanation of Intent

Sometimes a comment goes beyond just useful information about the implementation and provides the intent behind a decision.

//This is our best attempt to get a race condition
//by creating large number of threads.
for (int i = 0; i < 25000; i++) {

3.6 Clarification

Sometimes it is just helpful to translate the meaning of some obscure argument or return value into something that’s readable. In general it is better to find a way to make that argument or return value clear in its own right; but when its part of the standard library, or in code that you cannot alter, then a helpful clarifying comment can be useful.

assertTrue(a.compareTo(a) == 0); // a == a
assertTrue(a.compareTo(b) != 0); // a != b
assertTrue(ab.compareTo(ab) == 0); // ab == ab

3.7 Warning of Consequences

Sometimes it is useful to warn other programmers about certain consequences. For
example, here is a comment that explains why a particular test case is turned off:

//SimpleDateFormat is not thread safe,
//so we need to create each instance independently.
SimpleDateFormat df = new SimpleDateFormat("EEE, dd MMM yyyy HH:mm:ss z");

3.8 TODO Comments

It is sometimes reasonable to leave “To do” notes in the form of //TODO comments. TODOs are jobs that the programmer thinks should be done, but for some reason can’t do at the moment. It might be a reminder to delete a deprecated feature or a plea for someone else to look at a problem. It might be a request for someone else to think of a better name or a reminder to make a change that is dependent on a planned event. Whatever else a TODO might be, it is not an excuse to leave bad code in the system.

Nowadays, most good IDEs provide special gestures and features to locate all the TODO comments, so it’s not likely that they will get lost. Still, you don’t want your code to be littered with TODOs. So scan through them regularly and eliminate the ones you can.

3.9 Bad Comments

Most comments fall into this category. Usually they are crutches or excuses for poor code or justifications for insufficient decisions.

The following examples fall into this category:

3.10 Redundant Comments

Following is a simple function with a header comment that is completely redundant.
The comment probably takes longer to read than the code itself.

// Utility method that returns when this.closed is true. Throws an exception
// if the timeout is reached.
public synchronized void waitForClose(final long timeoutMillis)
throws Exception

3.11 Misleading Comments

Sometimes, with all the best intentions, a programmer makes a statement in his comments that isn’t precise enough to be accurate.

3.12 Mandated Comments

It is just plain silly to have a rule that says that every function must have a javadoc, or every variable must have a comment. Comments like this just clutter up the code, propagate lies, and lend to general confusion and disorganization.

* @param title The title of the CD
* @param author The author of the CD
* @param tracks The number of tracks on the CD
* @param durationInMinutes The duration of the CD in minutes
public void addCD(String title, String author,
int tracks, int durationInMinutes) {...

3.13 Journal Comments

Sometimes people add a comment to the start of a module every time they edit it. Nowadays, however, these long journals are just more clutter to obfuscate the module. They should be completely removed.

3.14 Noise Comments

Sometimes you see comments that are nothing but noise. They restate the obvious and provide no new information.

* Default constructor.
protected AnnualDateRule() {

3.15 Closing Brace Comments

If you find yourself wanting to mark your closing braces, try to shorten your functions instead.

try {
while ((line = in.readLine()) != null) {
charCount += line.length();
String words[] = line.split("\\W");
wordCount += words.length;
} //while
System.out.println("wordCount = " + wordCount);
System.out.println("lineCount = " + lineCount);
System.out.println("charCount = " + charCount);
} // try

3.16 Commented-Out Code

Others who see that commented-out code won’t have the courage to delete it. They’ll think
it is there for a reason and is too important to delete.

There was a time, back in the sixties, when commenting-out code might have been useful. But we’ve had good source code control systems for a very long time now. Those systems will remember the code for us. We don’t have to comment it out any more. Just delete the code. We won’t lose it.

4. Formatting

You should take care that your code is nicely formatted. You should choose a set of simple rules that govern the format of your code, and then you should consistently apply those rules. If you are working on a team, then the team should agree to a single set of formatting rules and all members should comply. It helps to have an automated tool that can apply those formatting rules for you.

4.1 Vertical Openness Between Concepts

Nearly all code is read left to right and top to bottom. Each line represents an expression or a clause, and each group of lines represents a complete thought. Those thoughts should be separated from each other with blank lines.

4.2 Vertical Distance

Concepts that are closely related should be kept vertically close to each other. Clearly this rule doesn’t work for concepts that belong in separate files. But then closely related concepts should not be separated into different files unless you have a very good reason. Indeed, this is one of the reasons that protected variables should be avoided.

4.3 Variable Declarations.

Variables should be declared as close to their usage as possible. Because our functions are very short, local variables should appear a the top of each function.

4.4 Dependent Functions.

If one function calls another, they should be vertically close, and the caller should be above the callee, if at all possible. This gives the program a natural flow. If the convention is followed reliably, readers will be able to trust that function definitions will follow shortly after their use.

4.5 Vertical Ordering

In general we want function call dependencies to point in the downward direction. That is, a function that is called should be below a function that does the calling. This creates a nice flow down the source code module from high level to low level.

As in newspaper articles, we expect the most important concepts to come first, and we expect them to be expressed with the least amount of polluting detail. We expect the low-level details to come last.

4.6 Horizontal Formatting

To make this hierarchy of scopes visible, we indent the lines of source code in proportion to their position in the hierarchy. Statements at the level of the file, such as most class declarations, are not indented at all. Methods within a class are indented one level to the right of the class. Implementations of those methods are implemented one level to the right of the method declaration. Block implementations are implemented one level to the right of their containing block, and so on.

Programmers rely heavily on this indentation scheme. They visually line up lines on the left to see what scope they appear in. This allows them to quickly hop over scopes, such as implementations of if or while statements, that are not relevant to their current situation. They scan the left for new method declarations, new variables, and even new classes. Without indentation, programs would be virtually unreadable by humans.

4.7 Team Rules

The title of this section is a play on words. Every programmer has his own favorite formatting rules, but if he works in a team, then the team rules.

A team of developers should agree upon a single formatting style, and then every member of that team should use that style. We want the software to have a consistent style. We don’t want it to appear to have been written by a bunch of disagreeing individuals.

5. Objects and Data Structures

Objects expose behavior and hide data. This makes it easy to add new kinds of objects without changing existing behaviors. It also makes it hard to add new behaviors to existing objects. Data structures expose data and have no significant behavior. This makes it easy to add new behaviors to existing data structures but makes it hard to add new data structures to existing functions.

In any given system we will sometimes want the flexibility to add new data types, and so we prefer objects for that part of the system. Other times we will want the flexibility to add new behaviors, and so in that part of the system we prefer data types and procedures. Good software developers understand these issues without prejudice and choose the approach that is best for the job at hand.

6. Error Handling

Error handling is important, but if it obscures logic, it’s wrong.

6.1 Use Exceptions Rather Than Return Codes

Back in the distant past there were many languages that didn’t have exceptions. In those languages the techniques for handling and reporting errors were limited. You either set an error flag or returned an error code that the caller could check.

The problem with these approaches is that they clutter the caller. The caller must check for errors immediately after the call. Unfortunately, it’s easy to forget. For this reason it is better to throw an exception when you encounter an error. The calling code is cleaner. Its logic is not obscured by error handling.

6.2 Provide Context with Exceptions

Each exception that you throw should provide enough context to determine the source and location of an error. In Java, you can get a stack trace from any exception; however, a stack trace can’t tell you the intent of the operation that failed.

Create informative error messages and pass them along with your exceptions. Mention the operation that failed and the type of failure. If you are logging in your application, pass along enough information to be able to log the error in your catch.

Clean code is readable, but it must also be robust. These are not conflicting goals. We can write robust clean code if we see error handling as a separate concern, something that is viewable independently of our main logic. To the degree that we are able to do that, we can reason about it independently, and we can make great strides in the maintainability of our code.

7. Boundaries

We seldom control all the software in our systems. Sometimes we buy third-party packages or use open source. Other times we depend on teams in our own company to produce components or subsystems for us. Somehow we must cleanly integrate this foreign code with our own.

Interesting things happen at boundaries. Change is one of those things. Good software designs accommodate change without huge investments and rework. When we use code that is out of our control, special care must be taken to protect our investment and make sure future change is not too costly.

Code at the boundaries needs clear separation and tests that define expectations. We should avoid letting too much of our code know about the third-party particulars. It’s better to depend on something you control than on something you don’t control, lest it end up controlling you.

We manage third-party boundaries by having very few places in the code that refer to them. We may use an ADAPTER to convert from our perfect interface to the provided interface. Either way our code speaks to us better, promotes internally consistent usage across the boundary, and has fewer maintenance points when the third-party code changes.

8. Unit Tests

Test code is just as important as production code. It is not a second-class citizen. It requires thought, design, and care. It must be kept as clean as production code.

If you don’t keep your tests clean, you will lose them. And without them, you lose the very thing that keeps your production code flexible. Yes, you read that correctly. It is unit tests that keep our code flexible, maintainable, and reusable. The reason is simple. If you have tests, you do not fear making changes to the code! Without tests every change is a possible bug. No matter how flexible your architecture is, no matter how nicely partitioned your design, without tests you will be reluctant to make changes because of the fear that you will introduce undetected bugs.

But with tests that fear virtually disappears. The higher your test coverage, the less your fear. You can make changes with near impunity to code that has a less than stellar architecture and a tangled and opaque design. Indeed, you can improve that architecture and design without fear!

So having an automated suite of unit tests that cover the production code is the key to keeping your design and architecture as clean as possible. Tests enable all the -ilities, because tests enable change.

Tests are as important to the health of a project as the productioncode is. Perhaps they are even more important, because tests preserve and enhance the flexibility, maintainability, and reusability of the production code. So keep your tests constantly clean. Work to make them expressive and succinct. Invent testing APIs that act as domain-specific language that helps you write the tests.

If you let the tests rot, then your code will rot too. Keep your tests clean.

9. Classes

Following the standard Java convention, a class should begin with a list of variables. Public static constants, if any, should come first. Then private static variables, followed by private instance variables. There is seldom a good reason to have a public variable.

Public functions should follow the list of variables. We like to put the private utilities called by a public function right after the public function itself. This follows the stepdown rule and helps the program read like a newspaper article.

9.1 Classes Should Be Small!

The first rule of classes is that they should be small. The second rule of classes is that they should be smaller than that. No, we’re not going to repeat the exact same text from the Functions chapter. But as with functions, smaller is the primary rule when it comes to designing classes. As with functions, our immediate question is always “How small?”

With functions we measured size by counting physical lines. With classes we use a different measure. We count responsibilities.

The name of a class should describe what responsibilities it fulfills. In fact, naming is probably the first way of helping determine class size. If we cannot derive a concise name for a class, then it’s likely too large. The more ambiguous the class name, the more likely it has too many responsibilities. For example, class names including weasel words like Processor or Manager or Super often hint at unfortunate aggregation of responsibilities.

9.2 The Single Responsibility Principle

We want our systems to be composed of many small classes, not a few large ones. Each small class encapsulates a single responsibility, has a single reason to change, and collaborates with a few others to achieve the desired system behaviors.

9.3 Cohesion

Classes should have a small number of instance variables. Each of the methods of a class should manipulate one or more of those variables. In general the more variables a method manipulates the more cohesive that method is to its class. A class in which each variable is used by each method is maximally cohesive.

In general it is neither advisable nor possible to create such maximally cohesive classes; on the other hand, we would like cohesion to be high. When cohesion is high, it means that the methods and variables of the class are co-dependent and hang together as a logical whole.

The strategy of keeping functions small and keeping parameter lists short can sometimes lead to a proliferation of instance variables that are used by a subset of methods. When this happens, it almost always means that there is at least one other class trying to get out of the larger class. You should try to separate the variables and methods into two or more classes such that the new classes are more cohesive.

10. Systems

Systems must be clean too. An invasive architecture overwhelms the domain logic and impacts agility. When the domain logic is obscured, quality suffers because bugs find it easier to hide and stories become harder to implement. If agility is compromised, productivity suffers and the benefits of TDD are lost.

Whether you are designing systems or individual modules, never forget to use the simplest thing that can possibly work.

11. Successive Refinement

It is not enough for code to work. Code that works is often badly broken. Programmers who satisfy themselves with merely working code are behaving unprofessionally. They may fear that they don’t have time to improve the structure and design of their code, but I disagree. Nothing has a more profound and long-term degrading effect upon a development project than bad code. Bad schedules can be redone, bad requirements can be redefined.

Bad team dynamics can be repaired. But bad code rots and ferments, becoming an inexorable weight that drags the team down. Time and time again I have seen teams grind to a crawl because, in their haste, they created a malignant morass of code that forever thereafter dominated their destiny.

Of course bad code can be cleaned up. But it’s very expensive. As code rots, the modules insinuate themselves into each other, creating lots of hidden and tangled dependencies. Finding and breaking old dependencies is a long and arduous task. On the other hand, keeping code clean is relatively easy. If you made a mess in a module in the morning, it is easy to clean it up in the afternoon. Better yet, if you made a mess five minutes ago, it’s very easy to clean it up right now.

So the solution is to continuously keep your code as clean and simple as it can be. Never let the rot get started.

clean code summary Rober Martin


pinte dan
About the Author

Dani Pinte

Dani is a software developer with over 10 years experience in developing Desktop and Web applications using Microsoft .NET Technologies. He is passionate about software, sports, travel and motorsport.

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