modeling

america's next top model

Just like this, only less beautiful women and more boring data

When solving a problem the most difficult part should almost never be the implementation. Implementing a solution should be fairly straightforward from the solution itself, if it is not, then your solution is incomplete. That doesn’t mean that you need to sit down with pen and paper and completely solve a problem before coding, this is an approach taken by some books attempting to teach software development. Some go as far to advocate for an even stricter approach, something along these lines.

  1. Model the problem domain with UML or some other modeling technique
  2. Translate your modeling into pseudo-code
  3. Translate pseudo-code into actual code
  4. Realize 2 month project took 2 years to complete
  5. Go to step 1 (the problem has radically changed since you took so long)

Of course, I’m poking fun at the books that take such a structured route. Here is a news flash for everyone Programmers are lazy. Here is another news flash (and this might actually be news to some people) Being lazy is good. Not all laziness is good, if it causes you to cut corners and fail to deliver a high quality product, than you fail at life. If however your laziness drives you to find an easier way to do things then “Lazy FTW!”

I have the “joy” of programming Java in my 9 to 5, when you write Java for a living you get used to the absurd amount of verbosity in your code, you also have the joy of Eclipse (notice no quotes around joy) that will write most of your Java code for you. Then there are things like Project Lombok that strive to make Java easier and more fun to write. C# got the memo, and let their programmers be lazy, let’s take a look at attributes in a few languages

Here is some C# code to make a class called Foo with a string member called Bar that can be read and written

public class Foo {
  public string Bar { get; set; }
}

They realized people are lazy, let’s look at the same thing in ruby

class Foo 
  attr_accessor :bar
end

Again, concise and simple, let’s look at some Java

public class Foo {
  private string bar;

  public string getBar() {
    return this.bar;
  }
 
  public void setBar(string bar) {
    this.bar = bar;
  }
}

Can you hear it? I can, it’s the language architect saying “Just don’t be so lazy, write those simple functions, it isn’t hard.” The truth of the matter is that it isn’t hard, in fact Eclipse will happily write these functions for you, just hit CTRL-SHIFT-G. I’m sure there is a point here that I’m getting to, and here it is. I don’t want to disparage Java or start a language war, what I want to point out is that Java has a different conceptual model than C# or ruby. C# has the concept of properties and ruby has the concept of attributes, Java doesn’t have anything like this, classes have members and functions, nothing more.

The point is that the conceptual models your solution adopts will have huge ramifications on the implementation of the solution. There are many examples of unifying philosophies that make things simpler. Unix’s concept that everything is a file, C++ concept of streams, Erlang’s pervasive share-nothing message passing, ruby’s duck typing. These are core concepts that have profound and far reaching consequences.

Working on a new side project I recently was struck by a way to simplify a complex data structure by adopting a new conceptual model. Creating an internally consistent, elegant conceptual model is the most important thing you can do for a project’s success.

Conceptual modeling is a hard thing to get right, go into too much detail and your model becomes rigid and brittle, go into too little and the model can be impossible to implement correctly. Making a good conceptual model is like making a good analogy, there are a few hallmarks to a good conceptual model.

  • The conceptual model should simplify the problem domain. Files and folders greatly simplifies the concept of a hierarchical file system by relating it to something commonplace.
  • The conceptual model should not be overly complex to translate into an implementation
  • The conceptual model should be uniform and consistent, if you find parts of the problem domain falling outside the conceptual model or feeling “tacked on” you should rework the model

The next time a project seems stuck or overly complex, instead of refactoring code or attempting to throw another library at it, take a step back, look at the big picture and refactor your conceptual mode.

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3 comments

  1. Timely post, I’m wrestling with a conceptual model for a wag at social/semantic artificial search intelligence (the things we do to try get folks on a page ;) . I’ve got this foggy image in my head, but making all the pieces play together isn’t very neat or tidy.

    Do you characterize your conceptual models by how you expect them to respond to stimuli (input)? Just curious, because thats how I usually begin.

  2. @Mark I normally try to look at the problem in the simplest way possible and then come up with some sort of conceptual model based around that. Too often we let our abstractions get in our way, we can’t separate the reality of the situation from the broken mental model we built up around it. I take a step back forget about all the little things that I’ve built up around these strings and numbers and look at it fresh.

    The experience of doing it once normally gives me some great insight into doing it better the second time.

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