[throwaw12]

> my first bet would be specifications and tests

You are missing another dimension how easy it would be to migrate if adding new feature hits a ceiling and LLM keeps breaking the system.

Imagine all tests are passing and code is confirming the spec, but everything is denormalized because LLM thought this was a nice idea at the beginning since no one mentioned that requirement in the spec. After a while you want to add a feature which requires normalized table and LLM keeps failing, but you also have no idea how this complex system works.

Don't forget that very very detailed spec is actually the code

[refactor_master]

> Don't forget that very very detailed spec is actually the code

The tests, sure. But certainly not the code itself, as that sits far too close to the implementation (i.e. it is the implementation). An almost infinite number of implantations can fulfill “does foo when bar”, so how can we prove that ours is the spec itself?

It’s kind of like a scientist coming up with a hypothesis post-hoc to fit the results of the experiment.

[demorro]

I know you were simplifying, but "does foo then bar" is so far away from what an actual specification is that it defeats the point.

A more complete spec will capture performance requirements, input preconditions and output postconditions, error handling and recovery behaviors, threading behaviours, hardware assumptions, etc. It's hard to do these things without leaning at least somewhat on the specific language runtime you are using, otherwise you'd end up regurgitating the C standard each time you design a software system.

It's this sort of stuff that is meant when people say "sufficiently detailed".

If you're actually testing all these things, then I might agree with you that you can do it in the tests, but almost no one actually is. I'd struggle to write a test suite that tests all the specification-level assumptions I draw from my language and target platforms.

[stuaxo]

I've found LLM written unit tests to be fairly low quality which makes sense as writing good tests takes understanding that writing good code.

Also the quality of tests in general in projects is often so so and that's reflected in the output of LLMs even more so.

[LtWorf]

It'd be insane to come up with theories before doing any sort of observation.

[refactor_master]

In the realm of statistics, not so. If you're right on the limit of detectability, robust experimental design is necessary to avoid assigning meaning to noise.

[abalashov]

> Don't forget that very very detailed spec is actually the code

Came here to say this, but you said it for me. If the problem were merely one of insufficient rigour or detail in specs, it would have been solved long before LLMs.

[theshrike79]

> Don't forget that very very detailed spec is actually the code

In the age of AI this is more true than you know. Given a detailed enough spec and test suite you can effectively rewrite any application with any language in a fully automated way.

I've coined that as "Duck coding" :D If it quacks like a duck, walks like a duck and looks like a duck - it's duck enough for as far as the spec is concerned. Does it matter what is inside the duck?

[npodbielski]

Yes. It would be like buying a car that you have no idea about the engine and gearbox and kind of fuel it is using or if at all. It have four wheels and and it can drive you from point A to B. Sure but sometimes it happens that some particular brands and they particular model requires engine renovation after 100k km because it is so shitty design.

Right now we are just starting vibe coded software, nobody knows how it will behave in 2 years or 5 or 10. My guess it won't. So we will enter age of scratch software. You build it, ship it. And after few months you will ship entirely new one. And then again. And again. And again. Because maintenance is hard and costly and writing from scratch will cost like 1k$ in tokens.

And users will have problem of migrating the data if possible at all. But if migration is hard and everything changes all the time does it even matter if you are using X o Y software? Does it even matter since you can write your own software and migrate your data there?

I think we saw how this ends with Chinese manufacturing. You buy some stuff from AliExpress for 2$ and throw it away in two weeks and buy a new one. So quality does not matter anymore.

[LtWorf]

The problem is that you cannot just throw away your data.

[skydhash]

People are so focused on code they forget that the actual values is data. We create software to transform data from what we have to what we want. A software that can’t do it or, worse, corrupt the data, is worthless.

[npodbielski]

I am talking about applications like to do lists. Or you running app. Or I do not know... Currency exchange calculator. People do not care about that data. Ordinary person maybe would be a bit sad about loosing emails but not terribly. Maybe half of people would be trying to get their picture back rest would just say 'oh well...' And moved on.

[LtWorf]

Ah yes, things that students make their first week. Surely developers aren't paid to do those?

[npodbielski]

I have no idea what you are talking about.