proptest VS julia

proptest: Property-based testing with random input generation.

Isn't proptest something that could handle this?

https://github.com/proptest-rs/proptest

Proptest is a Rust crate for property-based testing. Recently I wanted/needed to manually implement a proptest strategy for my own type, and I realized that there is not that much material on how to do it. So I wrote a post where I tried to describe what I learned. It's a bit niche, but I hope that someone at some point will find it useful.

Take a look at proptest.

Yes exactly, you can also add to this fuzzing and property based testing.

proptest is great! It generates random input data according to some rules, and if the input fails it saves random seed into a file so that failing inputs are guaranteed to be tested on the subsequent runs (as well as new random inputs). It also doesn't immediately stop on fail but tries to find a minimal failing input first.

The only other crate I could find is proptest, but it looks a lot more complicated, and I don't know if lets you skip the shrinking step as quickcheck does. I've been reading the book and going through the docs, but a quick answer would be appreciated.

We just released proptest 1.1.0, a property-testing framework for Rust. Proptest has recently found new maintainers, and this marks the first new release of proptest in ~2 years.

Hi, I'm working on a fuzzer, that fuzzes APIs based on OpenAPI specification. I'd like to implement shrinking. It means that when an interesting input (for the API) is found, I'd like to create the smallest possible input that still causes the same behaviour of the API. I'd like to implement a payload generation via proptest, because it already has the shrinking ability. I'm having issues implementing the JSON object as a proptest strategy. Here is what I tried so far. I explained it in a detail in stackoverflow question but it did not reach many people. Thanks for your help!

Third, if you're opting out of a compile-time safety guarantee in the name of performance, test heavily (high-coverage unit tests, property testing, fuzzing, differential fuzzing, etc.) and make use of tools like Loom and Miri's runtime data race detector for unsafe code, which can catch stuff that is beyond the scope of the compiler's guarantees.

34. Julia - $74,963

I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.

3. dispatch on all the arguments

the first solution is clean, but people really like dispatch.

the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.

the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.

the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.

nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html

lisps of course lack UFCS.

see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779

so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!

https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md

Visit official site: https://julialang.org/

No. It runs natively on ARM.

julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release

https://github.com/JuliaLang/julia/issues/51086#issuecomment...

So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.

But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.

Still, the musl author has some reservations against making `jemalloc` the default:

https://www.openwall.com/lists/musl/2018/04/23/2

> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.

With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".

I have asked about Julia's reproducibility story on the Guix mailing list in the past, and at the time Simon Tournier didn't think it was promising. I seem to recall Julia itself didnt have a reproducible build. All I know now is that github issue is still not closed.

https://github.com/JuliaLang/julia/issues/34753

I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.

However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.

The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.

Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.

Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...

I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...

but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...