- cross-posted to:
- [email protected]
- cross-posted to:
- [email protected]
cross-posted from: https://programming.dev/post/8121843
~n (@[email protected]) writes:
This is fine…
“We observed that participants who had access to the AI assistant were more likely to introduce security vulnerabilities for the majority of programming tasks, yet were also more likely to rate their insecure answers as secure compared to those in our control group.”
[Do Users Write More Insecure Code with AI Assistants?](https://arxiv.org/abs/2211.03622?
eh, I’ve known lots of good programmers who are super stuck in their ways. Teaching them to effectively use an LLM can help break you out of the mindset that there’s only one way to do things.
I find it’s useful when writing new code because it can give you a quick first draft of each function, but most of the time I’m modifying existing applications and it’s less useful for that. And you still need to be able to judge for yourself whether the code it offers is any good.
I find it’s great for explaining convoluted legacy code, it’s all about utilizing it effectively
It really depends
If you want to avoid these issues I’d suggest to first read the docs, then look up stack overflow or likely name of a function you need to write on grep.app, then use a LLM as your last resort. Good for prototyping usually, less so for more specific things.
I think that’s one of the best use cases for AI in programming; exploring other approaches.
It’s very time-consuming to play out how your codebase would look like if you had decided differently at the beginning of the project. So actually comparing different implementations is very expensive. This incentivizes people to stick to what they know works well. Maybe even more so when they have more experience, which means they really know this works very well, and they know what can go wrong otherwise.
Being able to generate code instantly helps a lot in this regard, although it still has to be checked for errors.