Your CSS seems to assume all portrait screens (whether 80" or 3") deserve the same treatment.
It also might be more transparent and cheaper.
On a side note: I have wondered whether LLM's are particularly good with functional languages. Imagine if your code entirely consisted of just pure functions and no side effects. You pass all parameters required and do not use static methods/variables and no OOP concepts like inheritance. I imagine every program can be converted in such a way, the tradeoff being human readability.
On statically typed languages this happens for free at compile time.
I’ve often heard proponents of dynamically typed languages say how all the typing and boiler plate required by statically typed languages feels like such a waste of time, and on a small enough system maybe they are right.
But on any significant sized code bases, they pay dividends over and over by saving you from having to make tests like this.
They also allow trivial refactoring that people using dynamically typed languages wouldn’t even consider due to the risk being so high.
So keep this all in mind when you next choose your language for a new project.
I firmly believe that the group of people who laud dynamically typed languages as efficient time-savers, that help shed drudge work involving typing, is tightly correlated with the group of people who fail to establish any form of quality assurance or testing, often using the same arguments to justify their motivation.
Of course, some programmers just don't care about purchasing reliability. Those are the ones who eschew type systems, and tests, and produce unreliable software, about like you'd expect. But for my purposes, this is besides the point.
Personally I do use type hinting and mypy for much of my Python code. But I'll most certainly omit it for throwaway scripts and trivial stuff. I'm still not convinced it's really worth the effort, though. I've had a few occasions where the type checker has caught something important, but most of the time it's an autist trap where you spend ages making it correct "just because".
Tests don't assert correctness. At best they verify specific invariants.
Statically typed languages lean on the compiler to automatically verify some classes of invariants (i.e., can I call this method in this object?)
With dynamically typed languages, you cannot lean on the compiler to verify these invariants. Developers must fill in this void by writing their own tests.
It's true that they "need" to do it to avoid some classes of runtime errors that are only possible in dynamically typed languages. But that's not the point. The point is that those who complan that statically typed languages are too cumbersome because they require boilerplate code for things type compile-time type checking are also correlated with the set of developers who fail to invest any time adding or maintaining automated test suites, because of the same reasons.
> I could just as easily say static people don't write tests because they think the type system is enough. A type system is laughably bad at asserting correct behaviour.
No, you can't. Developers who use statically typed languages don't even think of type checking as a concern, let alone a quality assurance issue.
https://danluu.com/empirical-pl/
>But on any significant sized code bases, they pay dividends over and over by saving you from having to make tests like this.
OK, but if the alternative to tests is spending more time on a reliability method (type annotations) which buys you less reliability compared to writing tests... it's hardly a win.
It fundamentally seems to me that there are plenty of bugs that types can simply never catch. For example, if I have a "divide" function and I accidentally swap the numerator and divisor arguments, I can't think of any realistic type system which will help me. Other methods for achieving reliability, like writing tests or doing code review, don't seem to have the same limitations.
As is it's neat that they wrote some code to generate some prompts for an LLM but there's no idea if it actually works.
I would also add the concern on whether the tests are actually deterministic.
The premise is also dubious, as docstring comments typically hold only very high-level descriptions of the implementation and often aren't even maintained. Writing a specification of what a function is expected to do is what writing tests is all about, and with LLMs these are a terse prompt away.
This is exactly the problem that TDD solves. One of the most compelling reasons for test-first is because "Running the code in your head" does not actually work well in practice, leading to the above-cited issues. This is just another variant of "Running the code in your head" except an LLM is doing it. Strong TDD practices (don't write any code without a test to support it) will close those gaps. It may feel tedious at first but the safety it creates will leave you never wanting to go back.
Where this could be safe and useful: Find gaps in the test-set. Places where the code was never written because there wasn't a test to drive it out. This is one of the hardest parts of TDD, and where LLMs could really help.
Broadly speaking, linters are good, and if you have a way of linting implementation errors it's probably helpful.
I would say it's probably more helpful while you're coding than at test/CI time because it will be, indubitably, flakey.
However, for a local developer workflow I can see a reasonable value in being able to go:
Take every function in my code and scan it to figure out if you think it's implemented correctly, and let me know if you spot anything that looks weird / wrong / broken. Ideally only functions that I've touched in my branch.
So... you know. Cool idea. I think it's overselling how useful it is, but hey, smash your AI into every possible thing and eventually you'll find a few modestly interesting uses for it.
This is probably a modestly interesting use case.
> suite allows you to run the tests asynchronously, and since the main bottleneck is IO (all the computations happen in a GPU in the cloud) it means that you can run your tests very fast. This is a huge advantage in comparison to standard tests, which need to be run sequentially.
uh... that said, saying that it's fast to run your functions through an LLM compared to, you know, just running tests, is a little bit strange.
I'm certain your laptop will melt if you run 500 functions in parallel through ollama gemma-3.
Running it over a network is, obviously, similarly insane.
This would also be enormously and time consuming and expensive to use with a hosted LLM api.
The 'happy path' is probably having a plugin in your IDE that scans the files you touch and then runs this in the background when you make a commit somehow using a local LLM of sufficient complexity it can be useful (gemma3 would probably work).
Kind of like having your tests in 'watch mode'; you don't expect instant feedback, but some-time-after you've done something you get a popup saying 'oh hey, are you sure you meant to return a string here..?'
Maybe it would just be annoying. You'd have to build it out properly and see. /shrug
I think it's not implausible though, that you could see something vaguely like this that was generally useful.
Probably what you see in this specific implementation is only the precursory contemplations of something actually useful though. Not really useful on its own, in its current form, imo.
And if you're really dead-set on paying nondeterminism to get more coverage, property-based testing has existed for a long time and has a comparatively solid track record.
I have the toughest time trying to communicate why f(x) should equal f(x) in the general case.
Maybe the non-determinism can be reduced by caching: Just reevaluate the spec if the code actually changes?
I think there are also other problems (inlining a verbal description makes the codebase verbose, writing a precise, non-ambiguous verbal description might be more work than writing unit tests)
That would be good anyway to keep the costs reasonable.
Seems of dubious value as unit tests. LLMs don't seem to be quite smart enough for that in my experience, unless your bugs are really as trivial as adding instead of multiplying, in which case god help you.
The docstring literally says it only works with positive integers, and the LLM is supposed to follow the docstring (per previous assertions).
> The problem is that traditional tests can only cover a narrow slice of your function’s behavior.
Property tests? Fuzzers? Symbolic execution?
> Just because a high percentage of tests pass doesn’t mean your code is bug-free.
Neither does this thing. If you want your code to be bug-free what you're looking for is a proof assistant not vibe-reviewing.
Also
> One of the reasons to use suite is its seamless integration with pytest.
Exposing a predicate is not "seamless integration with pytest", it's just exposing a predicate.
I always get the feeling that fundamentally our software should be built on a foundation of sound logic and reasoning. That doesn't mean that we cannot use LLMs to build that software, but it does mean that in the end every line of code must be validated to make sure there's no issues injected by the LLM tools that inherently lack logic and reasoning, or at least such validation must be on par with human authored code + review. Because of this, the validation cannot be done by an LLM, as it would just compound the problem.
Unless we get a drastic change in the level of error detection and self-validation that can be done by an LLM, this remains a problem for the foreseeable future.
How is it then that people build tooling where the LLM validates the code they write? Or claim 2x speedups for code written by LLMs? Is there some kind of false positive/negative tradeoff I'm missing that allows people to extract robust software from an inherently not-robust generation process?
I'm not talking about search and documentation, where I'm already seeing a lot of benefit from LLMs today, because between the LLM output and the code is me, sanity checking and filtering everything. What I'm asking about is the: "LLM take the wheel!" type engineering.
edit: of course, maybe that means we need a meta-suite, that uses a different LLM to tell you which tests you should write yourself and which tests you can safely leave to LLM review.
The problem with your assertion is that it fails to understand that today's software, where every single line of code was typed in by real flesh-and-bone humans, already fails to have adequate test coverages, let alone be validated.
The main problem with output from LLMs is that they were trained with the code written by humans, and thus they accurately reflect the quality of the code that's found in the wild. Consequently, your line of reasoning actually criticizes LLMs for outputing the same unreliable code that people write.
Counterintuitively, LLMs end up generating a better output because at least they are designed to simplify the task of automatically generating tests.
Will that test be perfect? No. But what is the alternative?
The problem with it takes several times more effort to verify code than to write it. This makes intuitive sense if you consider that the search space for the properties of code is much larger than the code for space. Rice theorem's states that all non trivial semantic properties of a program are undeniable.
"Semantics" is literally behavior under execution. This is syntactical analysis by a stochastic language model. I know the NLP literature uses "semantics" to talk about representations but that is an assertion which is contested [1].
Coming back to testing, this implicitly relies on the strong assumption of the LLM correctly associating the code (syntax) with assertions of properties under execution (semantic properties). This is a very risky assumption considering, once again, these things are stochastic in nature and cannot even guarantee syntactical correctness, let alone semantic. Being generous with the former, there is a track record of the latter often failing and producing subtle bugs [2][3][4][5]. Not to mention the observed effect of LLMs often being biased to "agree" with the premise presented to them.
It also kind of misses the point of testing, which is the engineering (not automation) task of reasoning about code and doing QC (even if said tests are later run automatically, I'm talking about their conception). I feel it's a dangerous, albeit tempting, decision to relegate that to an LLM. Fuzzing, sure. But not assertions about program behavior.
[1] A Primer in BERTology: What we know about how BERT works https://arxiv.org/abs/2002.12327 (Layers encode a mix of syntactic and semantic aspects of natural language, and it's problem-specific.)
[2] Large Language Models of Code Fail at Completing Code with Potential Bugs https://arxiv.org/abs/2306.03438
[3] SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? https://arxiv.org/abs/2502.12115 (best models unable to solve the majority of coding problems)
[4] Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT https://arxiv.org/abs/2304.10778
[5] Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions https://arxiv.org/abs/2308.02312v4
EDIT: Added references
cjfd•5h ago
motorest•4h ago
Actually writing the tests is far more effective, and doesn't require fancy frameworks tightly coupled with external services.
masklinn•3h ago