all the criticism has been said, all the takes been had.
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@elebertus
Ive read so much LLM code at this point, there are still patterns that are present but elude my understanding, but one thing that's clear is that there are foundational flaw categories that are not improved upon by model version and appear in wildly different projects using wildly different models and harnesses. Testing is a big nexus of those flaws. I am not close to what would be a satisfying explanation of the dynamics, but every project suffers fucked testing problems. -
So, look. One shot rewriting the whole test suite in another language is probably not great to do, but what happened here is so much worse than you are expecting.
https://github.com/RsyncProject/rsync/pull/903/
This does not "translate tests into pytest" or a unit testing framework, it writes its own testing framework where tests are whole python scripts that redefine basic test functions in every script. Surely there would be a single way to "run rsync and get the results" - nope, well, there is, but then every test file will randomly redefine its own
_run_and_capturefunction. So like now rsync needs a test suite for its test suite.If instead of telling an LLM to "rewrite the tests in python" you just searched "python testing" you would find the pytest docs. And then you would find examples. And then you could write fixtures to deduplicate all the prior shell script setup and teardown stuff, and so on. But since it was just "rewrite the tests in python" its now worse than before, and the odds of the rewrite actually being a 100% faithful translation are close to 0.
@jonny One thing I love about this and other posts linking to slop on GitHub is that more often than not I flat out can't follow the link because GitHub is not working.
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Here's an example from some code that was thrust at me this week. The rest of the tests try a bit harder to look like tests, but this one is perplexing.
What does it test? The function name suggests its a smoke test. LLMs love to call things smoke tests. That would suggest this would be an early-run test that fails loudly if some basic precondition - like having ffmpeg - fails. Or, I guess we are smoke testing the
ensure_ffmpegfunction? Anyway who knows. However we first check if ffmpeg or ffprobe are present, which is exactly whatensure_ffmpegdoes. If they aren't present, a warning tells us that ffmpeg/ffprobe are required for the video tests, which makes it seem like this should be a parameterizing test that controls which tests are run, which of course it does not do.So the test literally does nothing and cannot possibly fail, but says it does at least two things, because to an LLM something saying it does something is the same thing as it actually doing that thing.
@jonny I struggle to express how bleak this is.
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@jonny I struggle to express how bleak this is.
@jonny It's like everyone decided to take a bath in mercury and leaded gasoline.
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@jonny It's like everyone decided to take a bath in mercury and leaded gasoline.
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So rsync rewriting all the tests puts the entire project in play. Now the entire protective surface has been sloshed through a layer of probability, so the loop must accelerate. Followup PRs add more carveouts with lengthy LLM justifications that sound perfectly plausible but amount to an erosion of the protective surface. We go from cumulative improvement to a random walk.
@jonny I just lost my beer league hockey championship as the last shooter on a 14 round shoot out. I'm sitting in my driveway reading your thread. I'll need to read it again in the morning.
I don't remember why I followed you originally. But I love this thread.
This whole rsync thing is the most interesting thing that has come out of the ai bubble.
I had a negative feel for rsync after years ago reading a blog criticizing its sloppy design.
Yet I rely on it daily. I have so many questions.
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@jonny I just lost my beer league hockey championship as the last shooter on a 14 round shoot out. I'm sitting in my driveway reading your thread. I'll need to read it again in the morning.
I don't remember why I followed you originally. But I love this thread.
This whole rsync thing is the most interesting thing that has come out of the ai bubble.
I had a negative feel for rsync after years ago reading a blog criticizing its sloppy design.
Yet I rely on it daily. I have so many questions.
@poleguy
RIP on the shootout, hopefully the other team bought the beer and you got to pinch the other goalies cheek a bit. You'll get em next season -
@poleguy
RIP on the shootout, hopefully the other team bought the beer and you got to pinch the other goalies cheek a bit. You'll get em next season@jonny indeed, that's the right feeling!
We have sponsorship from a brewery, so the locker room beer (and custom jerseys) are "free."
But we sat at the bar with the other team. It is just a game after all.
Both sides had a good time. And we had fans cheering for both sides. And kids crashing the locker room to celebrate despite the loss... We shared our NA options. Can't ask for more.
I'd love to engage more on this thread technically... I have thoughts. Maybe Monday.
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I think the modal situation here is that the people are reading none or very little of what is being generated by the LLM, so the tests have a special role: Tests function as the pull arm on the slot machine, you just generate until tests pass, and that's a jackpot. Obviously that's meaningless when the tests are meaningless, so tests take on a very different meaning and role in slot machine coding.
Previously we would write careful test conditions that were based off some real problem or an understanding of what the code under test did, and had a specific thing they were intended to protect against. Tests move slow and are designed to protect us against the things we know can go wrong. When we learn of a new wrong thing, we add a test.
LLM tests have the form of tests but don't do the same thing. They often test nothing, and are just expressions of truisms that the probabilistic text space explored while generating. They have strongly worded names but end up actually asserting that basic language features work as expected. Because it is not us writing tests for ourselves, where we only harm ourselves by making them weak, they function instead as a passively obfuscated justification for the code that the LLM generates. The user wants the tests to pass. The LLM provides.
The tests are theater: they are the play field for the slot machine. They are mild, surmountable, need to fail a few times to be plausible, but must eventually pass within the expected generation loop window to deliver the payout.
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@ainmosni
Well good, keep those walls up, they are protective. I am not so lucky and rely on discipline and observation of the impacts on others. It sets my alarm bells ringing to run for cover, but to understand why the things are happening around me the only means has been to feel it for myself, and I get it.@jonny @ainmosni Gambling (addiction) works on the so-called Variable Reinforcement Schedule.
The TL;DR of it is, results are random enough that even though it seems there may be a pattern, there isn't. You're pulled in because "one more time will show my pattern detection was right".
And since human brains are excellent pattern detection machines, every time this succeeds yields huge dopamine rewards.
I'm pissed off with the pattern, which is why I stop. But I can't deny its power.
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So rsync rewriting all the tests puts the entire project in play. Now the entire protective surface has been sloshed through a layer of probability, so the loop must accelerate. Followup PRs add more carveouts with lengthy LLM justifications that sound perfectly plausible but amount to an erosion of the protective surface. We go from cumulative improvement to a random walk.
@jonny this whole thing is so bad that the only viable way seems to fork it before the LLM sloppening. It is a shame to see more and more foundational projects fall into the LLM trap.
And as always you hit the nail on the head with your deep dive and explanations. I love reading them.
I will use your observation on how for a LLM what is written is the same as what is happening.
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Here's an example from some code that was thrust at me this week. The rest of the tests try a bit harder to look like tests, but this one is perplexing.
What does it test? The function name suggests its a smoke test. LLMs love to call things smoke tests. That would suggest this would be an early-run test that fails loudly if some basic precondition - like having ffmpeg - fails. Or, I guess we are smoke testing the
ensure_ffmpegfunction? Anyway who knows. However we first check if ffmpeg or ffprobe are present, which is exactly whatensure_ffmpegdoes. If they aren't present, a warning tells us that ffmpeg/ffprobe are required for the video tests, which makes it seem like this should be a parameterizing test that controls which tests are run, which of course it does not do.So the test literally does nothing and cannot possibly fail, but says it does at least two things, because to an LLM something saying it does something is the same thing as it actually doing that thing.
@jonny Of course it's a smoke test - as in "smoke and mirrors"
WTAF.
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So rsync rewriting all the tests puts the entire project in play. Now the entire protective surface has been sloshed through a layer of probability, so the loop must accelerate. Followup PRs add more carveouts with lengthy LLM justifications that sound perfectly plausible but amount to an erosion of the protective surface. We go from cumulative improvement to a random walk.
@jonny also why the hell would they write tests for a C program/library in Python? It makes no sense.
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RE: https://hails.org/@hailey/116657391001259044
all the criticism has been said, all the takes been had. the only metaphor i have been finding consistently useful for understanding what is happening with people and "AI" is addiction, and specifically gambling addiction.
@jonny holy crap this story gets worse by the day. Thank you very much for summing-up this aspect of the situation for a non-sw-engineering-person like me. 🫡
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@jonny also why the hell would they write tests for a C program/library in Python? It makes no sense.
@jonny ... and why the everloving FUCK do these tests run as root
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I think the modal situation here is that the people are reading none or very little of what is being generated by the LLM, so the tests have a special role: Tests function as the pull arm on the slot machine, you just generate until tests pass, and that's a jackpot. Obviously that's meaningless when the tests are meaningless, so tests take on a very different meaning and role in slot machine coding.
Previously we would write careful test conditions that were based off some real problem or an understanding of what the code under test did, and had a specific thing they were intended to protect against. Tests move slow and are designed to protect us against the things we know can go wrong. When we learn of a new wrong thing, we add a test.
LLM tests have the form of tests but don't do the same thing. They often test nothing, and are just expressions of truisms that the probabilistic text space explored while generating. They have strongly worded names but end up actually asserting that basic language features work as expected. Because it is not us writing tests for ourselves, where we only harm ourselves by making them weak, they function instead as a passively obfuscated justification for the code that the LLM generates. The user wants the tests to pass. The LLM provides.
The tests are theater: they are the play field for the slot machine. They are mild, surmountable, need to fail a few times to be plausible, but must eventually pass within the expected generation loop window to deliver the payout.
@jonny Referencing
1. @shauna post based on @DGI about power dynamics & dysfunction between imaginary labour(iML) & interpretive labour(iNL)-https://www.rethinkingpower.info/how-interpretive-labor-straddles-the-gap-between-rules-and-reality/
2. Power, chapter 4 of Mary Parker Follet’s Dynamic administration - https://mastodon.social/@dahukanna/110643444784446704Presuming Productivity(P)=(iML/iNL)
dysfunctional power-over tool imposition e.g. LLM, factory production,etc
- Imagined abstract: 1 LLM PR/0 review units= ∞P
- Interpreted reality: 1 LLM PR/>10 review units=0.1P
-https://mastodon.social/@dahukanna/113230734549577353 -
@jonny ... and why the everloving FUCK do these tests run as root
@fluffy@plush.city @jonny@neuromatch.social running tests as root is fucking wild
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@jens, great article, thank you. Did you pull the lever "just one more time" and if so, did it get even worse?
@jonny, thank you for this thread and lots of your other threads on the topic.
Both help feeling that I'm not the ghost driver although these days there is lot of contraflow on my lane. Mostly at work where the AI fanboys/believers/addicts are at least way louder than the people trying to understand and keeping their code in maintainable shape.
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@jens, great article, thank you. Did you pull the lever "just one more time" and if so, did it get even worse?
@jonny, thank you for this thread and lots of your other threads on the topic.
Both help feeling that I'm not the ghost driver although these days there is lot of contraflow on my lane. Mostly at work where the AI fanboys/believers/addicts are at least way louder than the people trying to understand and keeping their code in maintainable shape.
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