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Abstracting Ideas with Methods
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We ve already seen one tool for dividing our code into manageable pieces: methods. A method is a piece of a program that encapsulates a particular behavior completely. It s worth understanding the benefits of methods, because the same principles apply to the classes and structs that are this chapter s main subject.
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You will often see the term function used instead of method; they re related, but not identical. A function is a method that returns something. Some methods just do some work, and do not return any value. So in C#, all functions are methods, but not all methods are functions.
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Methods offer a contract: if we meet particular conditions, a method will do certain things for us. Conditions come in various forms: we might need to pass arguments of suitable types, perhaps with limits on the range (e.g., negative numbers may not be allowed). We may need to ensure certain things about the program s environment maybe we need to check that certain directories exist on disk, or that there s sufficient free memory and disk space. There may be constraints on when we are allowed to call the method perhaps we re not allowed to call it if some related work we started earlier hasn t completed yet. Likewise, there are several ways in which a method can hold up its side of the bargain. Perhaps it will just return a string or a number that is the result of a calculation involving the method s inputs. It might change the state of some entity in our system in some way, such as modifying an employee s salary. It may change something about the system environment the method might install a new device driver, or change the current user s color scheme, for example. Some methods interact with the outside world by sending messages over the network. Some aspects of the contract are formalized a method s parameter list defines the number and type of arguments we need to pass, for example, and its return type tells us what, if anything, to expect as a return value. But most of the contract is informally specified we rely on documentation (or sometimes, conversations with the developer who wrote the method) to understand the full contract. But understand it we must, because the contract is at the heart of how methods make our lives easier. Methods simplify things for us in two ways. If we are the user of a method, then, as long as its internal implementation conforms to the contract, we can treat it as a black box. We call it, we expect it to work as described, and we don t need to worry about how it worked. All its internal complexity is hidden from us, freeing us to think about ideas like increase this employee s salary, without getting bogged down by details such as open a connection to the database and execute some SQL. If, on the other hand, we are the developer of a method, we don t need to worry about who might call us, and why. As long as our implementation works as promised, we can choose any means of implementation we like perhaps optimizing for speed, or size, or (more often than not) simplicity and maintainability. We can concentrate on details like whether we re using the right connection string, and whether the SQL query modifies the database as intended, without needing to ask ourselves questions like should we even be adjusting this particular employee s salary at all So, one objective of good design is to hide distracting details and expose a simple model to your client. This practice is called encapsulation, and it s harder than it looks.
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As is so often the case in life, making something look easy takes years of practice and hard work. It can also be a thankless task: if you devise a contract that is a model of clarity, people will probably think it was easy to design. Conversely, unnecessary complexity is often mistaken for cleverness.
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While methods are essential for achieving encapsulation, they do not guarantee it. It s all too easy to write methods whose contract is unclear. This often happens when developers do something as an afterthought it can be oh so tempting to add a bit of extra code to an existing method as a quick solution to a problem, but this risks making that s method s responsibilities less clear. A method s name is often a good indicator of the clarity of the contract if the name is vague, or worse, if it s an inaccurate description of what the method does, you re probably looking at a method that does a bad job of encapsulation.
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One of the great things about methods is that we can use them to keep breaking things into smaller and smaller pieces. Suppose we have some method called PlaceOrder, which has a well-defined responsibility, but which is getting a bit complicated. We can just split its implementation into smaller methods say, CheckCustomerCredit, AllocateStock, and IssueRequestToWarehouse. These smaller methods do different bits of the work for us. This general technique, sometimes called functional decomposition, has a long history in mathematics. It was explored academically in computing applications as early as the 1930s. Bearing in mind that the first working programmable computers didn t appear until the 1940s, that s quite a pedigree. In fact, it has been around for so long that it now seems obvious to most people who have had anything to do with computer programming. That s not the end of the story, though. Methods are great for describing the dynamics of a system how things change in response to particular input data (the method arguments), and the results of those changes (a function s return value, or a method s side effects). What they re not so good at is describing the current state of the system. If we examine a set of functions and a load of variables, how can we work out which pieces of information are supposed to be operated on by which functions If methods were the only tool available for abstraction, we d have a hard time telling the difference between the double that describes my blood pressure, and can be operated on by this method:
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and the double that describes my weight and can be affected by this method:
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As programs get ever larger, the number of system state variables floating around increases, and the number of methods can explode exponentially. But the problems aren t
just about the sheer number of functions and variables you end up with. As you try to model a more complex system, it becomes harder to work out which functions and variables you actually need what is a good decomposition of the system Which methods relate to one another, and to which variables
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