The real problem with LLM coding, in my opinion, is something much more fundamental than whether it can code correctly or not. One of the biggest problems coding faces right now is code bloat. In my 15 years writing code, I write so much less code now than when I started, and spend so much more time bolting together existing libraries, dealing with CI/CD bullshit, and all the other hair that software projects has started to grow.
The amount of code is exploding. Nowadays, every website uses ReactJS. Every single tiny website loads god knows how many libraries. Just the other day, I forked and built an open source project that had a simple web front end (a list view, some forms – basic shit), and after building it, npm informed me that it had over a dozen critical vulnerabilities, and dozens more of high severity. I think the total was something like 70?
All code now has to be written at least once. With ChatGPT, it doesn’t even need to be written once! We can generate arbitrary amounts of code all the time whenever we want! We’re going to have so much fucking code, and we have absolutely no idea how to deal with that.
This is so true. I feel like my main job as a senior software engineer is to keep the bloat low and delete unused code. Its very easy to write code - maintaining it and focusing on the important bits is hard.
This will be one of the biggest and most challenging problems Computer Science will have to solve in the coming years and decades.
I don’t think it’s gonna go that way. In my experience the bigger the chunk of code you make it generate the more wrong it’s gonna be, not just because it’s a larger chunk of code, it’s gonna be exponentially more wrong.
It’s only good for generating small chunks of code at a time.
It won’t be long (maybe 3 years max) before industry adopts some technique for automatically prompting a LLM to generate code to fulfill a certain requirement, then iteratively improve it using test data to get it to pass all test cases. And I’m pretty sure there already are ways to get LLM’s to generate test cases. So this could go nightmarishly wrong very very fast if industry adopts that technology and starts integrating hundreds of unnecessary libraries or pieces of code that the AI just learned to “spam” everywhere so to speak. These things are way dumber than we give them credit for.
Oh that’s definitely going to lead to some hilarious situations but I don’t think we’re gonna see a complete breakdown of the whole IT sector. There’s no way companies/institutions that do really mission critical work (kernels, firmware, automotive/aerospace software, certain kinds of banking/finance software etc.) will let AI write that code any time soon. The rest of the stuff isn’t really that important and isn’t that big of a deal it if breaks for a few hours/days because the AI spazzed out.
There’s the other half of this problem, which is that the kind of code that LLMs are relatively good at pumping out with some degree of correctness are almost always the bits of code that aren’t difficult to begin with. A sorting algorithm on command is nice, but if you’re working on any kind of novel implementation then the hard bits are the business logic which in all likelihood has never been written before and is either sensitive information or just convoluted enough to make turning into a prompt difficult. You still have to have coders who understand architecture and converting requirements into raw logic to do that even with the LLMs.
