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AI in software engineering at Google: Progress and the path ahead
81–90 of 245 posts
Re: AI in software engineering at Google: Progress and the path ahead
#82Earlier quoted context omitted.
It's a concern I have too, when I get tired, I start to just delegate to co-pilot suggestions as I get desperate, if I didn't have co-pilot, I'd probably just log off for the day. I actually don't really use copilot as I didn't find it that helpful, so I don't really have the problem anymore, but I could see it was a danger. Bit like driving when tired.
I look at is as asking an intern to do some work that I don't have time for. Do I have to check their work? Yes. Might I have to correct and guide the outcome? Again, yes. Am I going to ask them to implement something novel and groundbreaking? Not really, that'd be a disaster unless they are a prodigy. None of that removes my capacity, or any kind of danger.
I find this absolutely nothing like delegating to an intern personally. Interns usually do their best because they will be held accountable if they don't.
Copilot doesn't give a shit about that.
Re: AI in software engineering at Google: Progress and the path ahead
#83I wonder if AI code completion reduces the value of abstractions if boilerplate code is easy to generate. Will this lead to highly repetitive code ?
In Go it’s not uncommon to use code generation to recreate boilerplate code, especially before the introduction of generics. No human looks at this usually. And if they do, they find the code they’re looking for contained in a few files. I personally found this pattern pretty good and easy to reason about.
Re: AI in software engineering at Google: Progress and the path ahead
#84[flagged]
This is kind of like answering “which software engineers are excited about Stack Overflow?” with “mediocre to bad ones”. It’s A) insulting, but more importantly B) wrong — if I’m, say, a web developer who is trying to learn graphics programming, of course I’ll need help as I build a mental model of the domain.
Re: AI in software engineering at Google: Progress and the path ahead
#85Earlier quoted context omitted.
I’m not seeing any evidence that any of this is actually good for Google’s business. Their observations that half of code checked in is from suggestions by an LLM is not really surprising in a regimented dev platform with tons of boilerplate. That stat tells us nothing about actual code quality, development velocity, or skill curves over time, much less business impact. What product of Google’s has been improved by t…
What you say tracks - but I'm wondering what happens if they manage to unlock some meaningful velocity increase to the point where they can begin tackling other domains and shuttling out products at a higher rate...Agree with your thoughts on search - it's f'ing unusable now and frustrating to look at.
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Re: AI in software engineering at Google: Progress and the path ahead
#86As others have mentioned, unless you have a strong conscience and really know what you're doing, it's far too tempting to just accept AI-generated suggestions without really thinking, and IMHO losing that understanding is a dangerous path to go down. AI can only increase quantity, not quality. The industry desperately needs far more of the latter. Related: https://navendu.me/posts/ai-generated-spam-prs/
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Re: AI in software engineering at Google: Progress and the path ahead
#87Google has been really lost here over the last several years. When lambda and bard were already killer at producing reasonable code and OpenAI had scarcely released anything, there was an explicit embargo against using them internally. When Blake Lemoine was talking nonsense about lambda being conscious in June ‘22, these internal models were already killer at producing code. ChatGPT wouldn’t be released for another…
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Re: AI in software engineering at Google: Progress and the path ahead
#88As others have mentioned, unless you have a strong conscience and really know what you're doing, it's far too tempting to just accept AI-generated suggestions without really thinking, and IMHO losing that understanding is a dangerous path to go down. AI can only increase quantity, not quality. The industry desperately needs far more of the latter. Related: https://navendu.me/posts/ai-generated-spam-prs/
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Re: AI in software engineering at Google: Progress and the path ahead
#89From the screencast: > implement also for Days This fails to recognize that this is a bad feature that the Abseil library would explicitly reject (hence the existence of absl::CivilDay) [0], and instead perpetuates the oversimplification that 1 day is exactly 24 hours (which breaks at least twice every year due to DST). Which is to say: it'll tell you how to do the thing you ask it to do, but will not tell you that i…
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Re: AI in software engineering at Google: Progress and the path ahead
#90If there's anything in this press release to justify how "characters added by AI" is more true a reflection of quality than commit count is of productivity, I didn't see it. It's a short release and I read it twice, so if it was there I feel like I'd have noticed.
On a related note, Microsoft published a press release last year [1] where they seemed to suggest that 30% of accepted copilot suggests was a 30% productivity boost for devs.
> users accept nearly 30% of code suggestions from GitHub Copilot
> Using 30% productivity enhancement, with a projected number of 45 million professional developers in 2030, generative AI developer tools could add productivity gains of an additional 15 million “effective developers” to worldwide capacity by 2030. This could boost global GDP by over $1.5 trillion
They were probably just being disingenuous to drum up hype but if not they'd have to believe that:
1) All lines of code take the same amount of time to produce 2) 100% of a developer's job is writing code
[1]: https://github.blog/2023-06-27-the-economic-impact-of-the-ai...