Comment on Why are people seemingly against AI chatbots aiding in writing code?
corroded@lemmy.world 4 months ago
When it comes to writing code, there is a huge difference between code that works and code that works *well." Lets say you’re tasked with writing a function that takes an array of RGB values and converts them to grayscale. ChatGPT is probably going to give you two nested loops that iterate over the X and Y values, applying a grayscale transformation to each pixel. This will get the job done, but it’s slow, inefficient, and generally not well-suited for production code. An experienced programmer is going to take into account possible edge cases (what if a color is out of the 0-255 bounds), apply SIMD functions and parallel algorithms, factor in memory management (do we need a new array or can we write back to the input array), etc.
ChatGPT is great for experienced programmers to get new ideas; I use it as a modern version of “rubber ducky” debugging. The problem is that corporations think that LLMs can replace experienced programmers, and that’s just not true. Sure, ChatGPT can produce code that “works,” but it will fail at edge cases and will generally be inefficient and slow.
sugar_in_your_tea@sh.itjust.works 4 months ago
Exactly. LLMs may replace interns and junior devs, they won’t replace senior devs. And if we replace all of the interns and junior devs, who is going to become the next senior devs?
As a senior dev, a lot of my time is spent reviewing others’ code, doing pair-programming, etc. Maybe in 5-10 years, I could replace a lot of what they do with an LLM, but then where would my replacement come from? That’s not a great long-term direction, and it’s part of how we ended up with COBOL devs making tons of money because financial institutions are too scared to port it to something more marketable.
When I use LLMs, it’s like you said, to get hints as to what options I have. I know it’s sampling from a bunch of existing codebases, so having the LLM go figure out what’s similar can help. But if I ask the LLM to actually generate code, it’s almost always complete garbage unless it’s really basic structure or something (i.e. generate a basic web server using <framework>), but even in those cases, I’d probably just copy/paste from the relevant project’s examples in the docs.
That said, if I had to use an LLM to generate code for me, I’d draw the line at tests. I think unit tests should be hand-written so we at least know the behavior is correct given certain inputs. I see people talking about automating unit tests, and I think that’s extremely dangerous and akin to “snapshot” tests, which I find almost entirely useless, outside of ensuring schemas for externally-facing APIs are consistent.