tricorder VS php-easycheck

There is https://pyinfra.com/

As a sidenote, I also made a small experiment a while ago : https://github.com/linkdd/tricorder/

But it's a bit of a chicken-and-egg problem. Without users, I don't know how it should be used, without features I won't get any users. So for now, it's in a state of "I'll address bug reports and feature requests, but I won't actively develop it".

Thank you. Your library seems nice.

Unfortunately, I have to say it... I hate YAML with a passion, k8s, github actions/gitlab ci, ansible, etc... When I'm doing ops jobs, I feel like I'm coding in YAML.

Btw, this hate for YAML birthed https://linkdd.github.io/tricorder/ :P

Another one in the same vein is tricorder, which has one of the coolest websites ever. AFAIK it doesn't do dependencies, though. But it's got a RUST API!

I tend to use anything that offers property-testing, since tests are much shorter to write and uncover lots more hidden assumptions.

My go-to choices per language are:

- Python: Hypothesis https://hypothesis.readthedocs.io/en/latest (also compatible with PyTest)

- Scala: ScalaCheck https://scalacheck.org (also compatible with ScalaTest)

- Javascript/Typescript: JSVerify https://jsverify.github.io

- Haskell: LazySmallCheck2012 https://github.com/UoYCS-plasma/LazySmallCheck2012/blob/mast...

- When I wrote PHP (over a decade ago) there was no decent property-based test framework, so I cobbled one together https://github.com/Warbo/php-easycheck

All of the above use the same basic setup: tests can make universally-quantified statements (e.g. "for all (x: Int), foo(x) == foo(foo(x))"), then the framework checks that statement for a bunch of different inputs.

Most property-checking frameworks generate data randomly (with more or less sophistication). The Haskell ecosystem is more interesting:

- QuickCheck was one of the first property-testing frameworks, using random genrators.

- SmallCheck came later, which enumerates data instead (e.g. testing a Float might use 0, 1, -1, 2, -2, 0.5, -0.5, etc.). That's cute, but QuickCheck tends to exercise more code paths with each input.

- LazySmallCheck builds up test data on-demand, using Haskell's pervasive laziness. Tests are run with an error as input: if they pass, we're done; if they fail, we're done; if they trigger the error, they're run again with slightly more-defined inputs. For example, if the input is supposed to be a list, we try again with the two forms of list: empty and "cons" (the arguments to cons are both errors, to begin with). This exercises even more code paths for each input.

- LazySmallCheck2012 is a more versatile "update" to LazySmallCheck; in particular, it's able to generate functions.