This is why the choice of language has a big impact on how much design work you'll do on paper before you begin coding. The more type checking and static analysis the compiler does and the more expressive the language is then the easier it is to organize and isolate code. Imagine that languages could be placed on a graph like the one below. Here we see that assembly languages require the most planning in advance of coding, but DSLs Domain Specific Languages can be so specialized for their task that you can jump into coding almost right away.
Even if your problem lives at the bullseye of a language's target there will still never be a language so expressive that you'll never need any kind of paper-planning. There will only ever be languages that make it safer to move into coding earlier. All programs transform input into output , even if that input was hard-coded into the source at design-time. It's obvious enough to be forgotten and programmers can get so lost in the fuss of implementing features that they forget what the real purpose of the program was.
Proper design can help you and your team mates avoid losing sight of the real goal. The code that does the conversion is called the business logic. The business logic will act on data structures called the model. Your program will need to convert or "deserialize" its input into the model, transform it, and then serialize the model into output. The model will be whatever makes it easiest to perform the transformation and does not need to resemble what the user thinks of the data as.
For example, a calculator converts decimal numbers into binary, performs arithmetic on them there, and then converts the binary back into decimal. The binary representation of the decimal numbers is the calculator's model. Everything else is called overhead , and that will be code that implements the user interface, sanitizes the input, creates connections to services, allocates and disposes of memory, implements higher-level data structures like trees and lists and so-on.
One of your goals will be to separate the overhead from the business logic so that you can make changes to one without having to modify the other. Don't create a class or module called "BusinessLogic" or similar. It shouldn't be a black-box. Later I'll discuss how to structure this code Don't put business logic in a library 1.
You'll want the ability to change it more often than the overhead, and libraries tend to get linked to other programs--multiplying the consequences of changing it Don't put business logic or overhead into the model unless it's only enforcing constraints. The code that defines the model should expose some interfaces that let you attach what you need. More on that later Some of the things that you can include in the model is code that enforces meaningful relationships. So if your genology program has a "Person" class with an "Offspring" collection then you should include code in the model that prevents a person from being their own grandparent.
But the function to discover cousin relationships in a family tree is business logic and doesn't belong in the model. Although it might make sense that you can't be your own cousin, and that this rule could be enforced in the model, you also need to draw a line between what enforcement is necessary to prevent common bugs and what's going to create complex bugs through mazes of logic that are difficult to follow. This rule is meant to apply to the point you're at in the food chain.
Top-Down programming is about writing the business logic first in pseudocode or non-compiling code and then working down to the specifics, and it's the first program design technique taught in schools. Lets say that you're designing a coin-counting program, and you write what you want the perfect main to look like:.
Bottom-up programming is the natural opposite, where the programmer intuits that he'll need a way to control the coin counting hardware and begins writing the code for that before writing the code for main. One of the reasons for doing it this way is to discover non-obvious requirements that have a major impact on the high-level design of the program. Lets say that when we dig into the nitty-gritty of the API for the hardware we discover that it doesn't tell us what type of coin is there, it just gives us sensory readings like the diameter and weight.
Now we need to change the design of the program to include a module that identifies coins by their measurements. What usually happens in the real-world is that all of the top-down design is done on paper and the actual coding goes bottom-up. Then when the coding effort falsifies an assumption made on paper the high-level design is easier to change.
Given a problem, there are certain traditional ways of solving it. Having windows on two adjacent walls in a room is a design pattern for houses that ensures adequate light at any time of day. Or putting the courthouse on one side of a square park and shops around the other sides is another kind of design pattern that focuses a town's community. The culture of computer programming has developed hundreds of its own design patterns: high level patterns that affect the architecture of the whole program--forcing you to chose them in advance--and low-level patterns that can be chosen later in development.
High-level patterns are baked into certain types of frameworks. Net, but that doesn't mean you have to use it for every program; some programs don't have to support random user interaction and wouldn't benefit from MVC. MVC is well described elsewhere , so here are some of the other high-level patterns and what they're good for.
This is the first pattern you learned in programming class: the program starts, asks the user for input, does something with it, prints it out and halts. It's the simplest pattern and still useful and appropriate in many situations. It's good for utilities, housekeeping tasks and one-off programs. One of the biggest mistakes made by programmers is to get too ambitious. They start designing an MVC program, take weeks or months to code and debug it, but then it dwarfs the magnitude of the problem.
If someone wants a program that doesn't need to take its inputs randomly and interactively, then produces a straightforward output, then it's a candidate for REPL is the kicked up a notch. Here the program doesn't halt after printing its output, it just goes back and asks for more input. But if you give the REPL the ability to maintain state between each command then you have a powerful base to build simple software that can do complex things. The design of a REPL program should keep the session's state separate from the code that interprets commands, and the interpreter should be agnostic to the fact that it's embedded in a REPL so that you can reuse it in other patterns.
Write results. This is so you can easily adapt the program to work on terminals, GUIs, web interfaces and so-on. Input is transformed one stage at a time in a pipeline that runs continuously. It's good for problems that transform large amounts of data with little or no user interaction, such as consolidating records from multiple sources into a uniform store or producing filtered lists according to frequently changing rules.
A news aggregator that reads multiple feed standards like RSS and Atom , filters out dupes and previously read articles, categorizes and then distributes the articles into folders is a good candidate for the Pipeline pattern, especially if the user is expected to reconfigure or change the order of the stages. You write your program as a series of classes or modules that share the same model and have a loop at their core.
The business logic goes inside the loop and treats each chunk of data atomically--that is, independent of any other chunk. If you're writing your program on Unix then you can take advantage of the pipelineing system already present and write your program as a single module that reads from stdin and writes to stdout.
Some language features that are useful for the Pipeline pattern are coroutines created with the "yield return" statement and immutable data types that are safe to stream.
This is similar to the Pipeline pattern but performs each stage through to completion before moving onto the next. It's good for when the underlying data type doesn't support streaming, when the chunks of data can't be processed independently of the others, when a high degree of user interaction is expected on each stage, or if the flow of data ever needs to go in reverse. The Workflow pattern is ideal for things like processing purchase orders, responding to trouble tickets, scheduling events, "Wizards", filing taxes, etc..
You'd wrap your model in a "Session" that gets passed from module to module. A useful language feature to have is an easy way to serialize and deserialize the contents of the session to-and-from a file on disk, like to an XML file. The user prepares the steps and parameters for a task and submits it.
The task runs asynchronously and the user receives a notification when the task is done. It's good for tasks that take more than a few seconds to run, and especially good for jobs that run forever until cancelled. Google Alerts is a good example; it's like a search query that never stops searching and sends you an email whenever their news spider discovers something that matches your query. Yet even something as humble as printing a letter is also a batch job because the user will want to work on another document while your program is working on queuing it up.
Your model is a representation of the user's command and any parameters, how it should respond when the task has produced results, and what the results are. You need to encapsulate all of it into a self-contained data structure like " PrintJobCriteria " and " PrintJobResults " classes that share nothing with any other object and make sure your business logic has thread-safe access to any resources so it can run safely in the background.
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