Let’s take a look into a few other different methodologies in software development. So this one is in complete contrast to the waterfall model of software development and pretty much all other development methodologies. Because this methodology, there really isn’t anything to it. It’s generally known as cowboy coding or code and fix. Now, this in many ways, is the anti software engineering approach, where planning, testing documentation at pretty much everything, all of that is ignored, or immediately writing code. And predictably, this leads to a lot of what we refer to as spaghetti code. And so it’s just mangled structure that has really no clear way to it at sprinkled with sub optimal algorithms memory leaks, structural issues is just a mess, right? Imagine getting out a box of Christmas lights that were just thrown into a box and in January, and you go back in December, try to put those Christmas lights up. And magically, they’ve aimed together into this big ball that takes hours upon hours to actually unravel. And sometimes it’s just easier to throw it away and start from scratch.
Even more unfortunately, with this methodology of cowboy coding, this is actually unfortunately very much how most students learn to approach software development and their early assignments. And it’s often carried with them into industry. So the sooner that we can actually start to teach design structure and this process, the better habits that you will develop, especially as you start getting closer to working to working to an internship and into To a full time job.
But not surprisingly, issues in software engineering are typically coupled with periods of surging growth for a field of computer science. And so this graph here based off of data collected by a tabulate survey tracks the growth of Bachelor’s of Computer Science degrees since 1966, up through about 2010. Now, you’ll notice the two very clear spikes in this graph, one peaking around 1985 and the second around 2004. That first rise was a big issue here for K state and many other universities that that matter, to and to meet the growing demand of students with the limited faculty available to graduate students taught and many of the undergraduate courses, similar measures were employed at other institutions likely compromising the quality of preparation for an entire generation of software engineers because people were thrown into teaching positions that really weren’t that well qualified to teach those particular topics are experienced enough.
A similar trend occurred during the second peak, although thankfully not so much with us. But hopefully you can maybe guess what the driving force was behind that growth rates at that time, right. So this peaked around 2004. That was around the.com. Boom. So the first search that we saw began in the early 1980s when the personal computer started to be introduced. And so that spikes the first surge and the demand for computer science degrees, and then the second peak reflects the dot com bubble. So as the internet came out in 1990, we saw a huge burst in the late 90s of companies thinking, Ah, well, we can make it big if we get on the internet. Okay, and so we saw a huge surge In the need for computer science. But after the.com bubble burst, which burst in the early 2000s, we saw a rapid decline in the demand for computer science degrees. But thankfully, over the past few years, we’ve since then we have seen a increase, and the demand for computer science or an increase in the number of degrees awarded in computer science. And we haven’t seen that die off quite yet.
But with the growth of the internet, Internet companies and the eventual collapse and the success of all the survivors to underscore the need for a more reactive and flexible style of software development that was possible with the waterfall model. So the dot com boom, sparked a lot of different kinds of needs of technology and software, especially with the invention of the internet, of which we had never actually developed software for something like that before. So the web needed to be what robust, secure and most importantly, scalable. And so that meant cowboy coding couldn’t meet that need either, even though it was super flexible, because we could just start with no planning whatsoever. But that often led to very insecure software and software that really didn’t scale well. So new strategies as a result of the dot com boom, emerged to create more robust software engineering approaches, and these were started to be employed more widely.
One of these of course, was just simply prototyping which is especially useful for developing experimental or difficult to estimate systems. And so the idea here right is to build a prototype, establish the base functionality, complexity and worth wildness of the of the specific product right. So this begins with implementation or coding and then leads to other activities, like requirements gathering and software design. So try something out and see if it works. See If the Customer likes it. If they liked Or let’s say they like this, but not that let’s keep things that they like and throw away the stuff they don’t want. So this is pretty much the cowboy coding or the code and fixed process with a more developed and structured approach, right. It’s not just a standalone process. There’s a lot more involved and there’s a lot more structure here.