Sunday, July 17, 2011

Putting the "science" back in computer science

Having transitioned my career into academia and game programming, I'm starting to find that my original intents with blogging really no longer fit my interests much anymore. I still love Agile and also finding better ways to solve problems and write code, but I'm finding that I'm far more interested in algorithms and data structures in general, as well as problems dealing with issues in game programming. I'm taking on topics in networking, graphics, AI, and even *gasp* math.

As a result, I'm going to start blogging more about these things starting now (and also make an effort to blog more regularly). As always, I hate blogging about specific technologies and how to use them, instead preferring a more generalist approach so that my posts are useful to anyone writing code to solve problems and not just how to do things with particular technologies or platforms. In fact, I honestly don't care anymore at all about things like platforms, libraries, and frameworks, other than looking at better ways to write such things.

We've forgotten the "science" part. . .

I feel a lot like developers these days have strayed from fundamentals of things like algorithms and data structures. When was the last time you implemented a hash table? A linked list? Quicksort? Never, because we have all kinds of nifty stuff that handles this for us. That said, it's still important to really know how these things work and I think it's good practice to implement these things as well, if for no other reason than self-improvement.

Justin Bozonier had an interesting blog post recently talking about code katas and that a kata is supposed to be narrow and focused, and that some of the TDD katas are overly complex, but also involve problem solving, which makes it more difficult to tell if you've done things "right." I feel like algorithms at their core are kata; there is one algorithm that is quicksort, but it has many applications and many flavors. The core of all flavors of quicksort is still quicksort, so it's good practice to be able to code quicksort, as it is easy to tell if you got it right and a deeper understanding of it will help you to understand different ways to apply it to your particular problem. This isn't to say that you should always be rigid and say "we're using quicksort to solve this problem so you implement it this way because that's what you do with quicksort" as this is the same problem that I have with people "doing" Agile or "implementing" design patterns. Design patterns, Agile, and algorithms are just guidelines for ways to solve particular problems, not rules, and as such it's important to know these things. They are the fundamentals of what we do as software engineers and computer scientists.

Stand back, I'm going to try science!

For the future, I'm going to focus each of my blog posts on a particular algorithm, data structure, or even mathematical concept. I'll explain the problem that it solves, how the algorithm works, and attempt to show an implementation of it in some programming language (I'll try to vary this but expect a lot of C/C++/C# for a while, with maybe some python thrown in here and there).

My hope is that by reading these, you'll remember your computer science classes and be able to arm yourself with all sorts of nifty ways to solve problems that you can stash in your toolbox just in case they ever come up. After all, knowledge is power.

1 comment:

Hire ASP.NET Developers said...

Well, Computer Science in the educational world is more like traditional science and/or mathematics. It's about experimentation and advancing a field of study strictly for the chase of knowledge.
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