Comment on AI chatbots were tasked to run a tech company. They built software in under seven minutes — for less than $1.

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Puzzle_Sluts_4Ever@lemmy.world ⁨1⁩ ⁨year⁩ ago

Management and sound technical specifications, that sounds to me like you’ve never actually worked in a real software company.

Worked at quite a few. Once you get out of college and start engaging with companies beyond “Ugh, how dare they want me to waste my precious time by talking to people” you start to learn the value of a strong management team.

And, more importantly, where those jira tickets come from.

A bog standard development flow is “all pull requests are linked to a documented issue/ticket. All pull requests require tests to pass, code coverage to not decrease, and approval by a code owner”

How does that work in reality?

Issues/tickets (just going to say issues from here on out) are created by a combination of customer feedback, identified issues by the development team, and directives from on high (which is generally related to the overall roadmap). One or more developers work on a merge request, the person who best understands the appropriate code looks it over, it is tested, and it is merged in. After enough of those cycles happen, a release is prepared and a manager signs off on it.

How does that map to an “AI” based workflow?

Issues/tickets (just going to say issues from here on out) are created by a combination of customer feedback, identified issues by the development team, and directives from on high (which is generally related to the overall roadmap). Because LLMs can provide feedback and uncertainty measurements once you get past Google Bard. And regression testing and nightly performance testing can highlight deficiencies. The issue is put into a template, that includes all existing constraints, and the LLM generates a solution. Someone who understands the code checks to make sure that looks sane, it is tested, and it is merged in. After enough of those cycles happen, a release is prepared and a manger signs off on it.

And then it becomes a question of what level you start requiring humans. Because when I do a code review prior to a Release? I am relying VERY heavily on my team to have been doing their due diligence. I skim through the MRs and look for a few hot spots but it is mostly “Well, Fred and Nancy said this was good and it passes all the tests so…”

You just said what the main problem is: ChatGPT is not perfect. Code that isn’t perfect (compiles + has consistent logic) is worthless. If you need a developer to look over it you’ve already lost and it would be faster to have that developer write the code themselves.

I VEHEMENTLY disagree with this. If you don’t have developers looking over your code then you are not a software engineer. And if it takes them the same amount of time to review code as it does to write it? You aren’t working on interesting problems and are wasting vast amounts of money.

I can farm out a general task of “improve our code coverage” to an intern. They can spend a few days (or even weeks) doing that, and I can review their MRs in a few minutes. If something looks weird, I leave a comment and wait for them to get back to me. All the time I am working on much more interesting problems… or doing the same for my SSEs.

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