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A study on robustness and reliability of large language model code generation

arxiv.org

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Re: A study on robustness and reliability of large language model code generation

#51

Post by tomjakubowski »

xtagon wrote: post #48
throwaway09223 wrote: post #45

Earlier quoted context omitted.

You have to put it in context: How good is the human alternative? I have interviewed hundreds of engineers across the entire skill spectrum. I think GPT4 right now is about on-par with a mid level developer. It makes a lot of mistakes, especially around counting, and usually can notice and fix them if they're pointed out. Humans do this all the time -- how often do you get a compiler error from something silly? It of…

A mid-level human engineer can iteratively fix the mistakes they start making, instead of that just being the final result. Can GPT-4 do that?

I wonder if anyone has done a feedback loop with GPT and compiler errors to train this sort of thing

Re: A study on robustness and reliability of large language model code generation

#52

Post by Yiin »

wredue wrote: post #44
keyle wrote: post #8

Ok but what are we calling API misuses? We collect 1208 coding questions from StackOverflow on 24 representative Java APIs. We summarize thecommon misuse patterns of these APIs and evaluate them oncurrent popular LLMs. The evaluation results show that evenfor GPT-4, 62% of the generated code contains API misuses,which would cause unexpected consequences if the code isintroduced into real-world software. Should have u…

I just don’t see how it could save time. Programmers nearly universally agree that reading code is harder than writing it. So when you have chat GPT writing your code, you have to read and understand it to ensure it’s actually doing what you need it to without awkward bugs or problems. Given that it’s harder to read than write code, it seems to stand to reason that the “time savings” must be nil.

To write the code you need to know what to write. In many cases I find it easier to read proposed solution to my problem and then figure out from there how I actually want to implement a solution. It's great starting point.

Re: A study on robustness and reliability of large language model code generation

#53

Post by skepticATX »

Gigachad wrote: post #14

I feel like AI for programming has been so overhyped. I've attempted to use ChatGPT for programming so many times, and almost every time it's just wasted my time. Giving me outright lies or generating stuff that looks right but doesn't work. And just throwing out it's answer and starting from scratch is faster than fixing its output. I've only found it useful for explaining basic terminology or concepts for a topic I…

There's a big gap between people using it to write greenfield applications and/or smaller tools, and people working on large codebases.

I am in the latter group, and I don't find it all that helpful. There simply aren't any tools that can plug into a massive codebase with millions of lines. I never just work on one specific repository either - a change generally involves multiple repositories.

On the other hand, if you're writing a smaller standalone utility, or working on a greenfield application, it can be helpful (with all the useful caveats about hallucations).

I think that a lot of ML folks, especially those in research, fall into the former category, which is why there has been so much hype.

Re: A study on robustness and reliability of large language model code generation

#54

Post by rvz »

I can guarantee you that no-one here fully trusts any LLM to write complicated bug free code without you (a human) to keep checking over it.

Anyone with an understanding of unexplainable black-box AIs knows that LLMs hallucinating is not 'the same thing as a human'. Humans can be held accountable for their mistakes and can explain themselves transparently. LLMs fundamentally cannot reason or explain themselves transparently, other than rewording its original answer(s) to make themselves sound credible; like an expert sophist.

It goes to show that this overhyped snake-oil is now at the late stage peak of inflated expectations of the Gartner hype cycle.

Re: A study on robustness and reliability of large language model code generation

#56

Post by tofukant »

corethree wrote: post #47
Gigachad wrote: post #14

I feel like AI for programming has been so overhyped. I've attempted to use ChatGPT for programming so many times, and almost every time it's just wasted my time. Giving me outright lies or generating stuff that looks right but doesn't work. And just throwing out it's answer and starting from scratch is faster than fixing its output. I've only found it useful for explaining basic terminology or concepts for a topic I…

The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…

100-62=38%

Re: A study on robustness and reliability of large language model code generation

#57

Post by tedunangst »

splatzone wrote: post #41

Run the code it writes, if it gives an error, paste it into the chat and 90% of the time the LLM can fix the issue. People are missing the point here - it's not about writing code in one-shot. An LLM-enabled loop can generate the code and test and refine it until it works

Someone posted a transcript of having chatgpt write them a bash script with the comment "see how much easier this was than figuring it out" and it took like a dozen tries of pasting error messages back in. I was infuriated just reading it. I cannot imagine trying to develop this way. But if people like it, whatever blows your hair back.

Allegedly the benefit is it will do the boring stuff like write the error handling code for you, but I have no faith it would work if it takes so many tries to get the happy path correct.

Re: A study on robustness and reliability of large language model code generation

#58

Post by albedoa »

tofukant wrote: post #56
corethree wrote: post #47

Earlier quoted context omitted.

The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…

100-62=38%

[flagged]

Re: A study on robustness and reliability of large language model code generation

#59

Post by ajross »

I mean, of course? It's trained on real world code. Real world code "misuses" APIs at, sure, a 62% rate. Sounds roughly right to me. Obviously there's dithering to be done over what constitutes "misuse" vs. actual bugs, etc... But really this sounds unsurprising.

AI isn't going to give you perfect code (or answers, or anything really). It's going to give you typical code, based on extremely broad "intuition" about how others ("all" others, really) have solved (or answered) the same problem. And that has value.

But it's not going to produce something better than the existing consensus, by definition.

Re: A study on robustness and reliability of large language model code generation

#60

Post by [deleted] »

corethree wrote: post #47
Gigachad wrote: post #14

I feel like AI for programming has been so overhyped. I've attempted to use ChatGPT for programming so many times, and almost every time it's just wasted my time. Giving me outright lies or generating stuff that looks right but doesn't work. And just throwing out it's answer and starting from scratch is faster than fixing its output. I've only found it useful for explaining basic terminology or concepts for a topic I…

The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…

[deleted]
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