pynguin VS Polyester.jl

https://swagger.io/specification/ https://github.com/se2p/pynguin

I would use black to reformat this, then, if you can't refactor/rewrite (which is a lot of work!) I would try automated test generation via something like pynguin or fuzzing. I mean … this is not going to be a reliable solution anyways if the codebase is like that. So I would go in a direction that I find interesting to learn about and that could be helpful for the project. That would be generating tests and doing fuzzing. In the end you should run some linters anyways so that you can justify your results and show that the task is not in the scope of an internship and needs extensive refactoring.

The main difference that Klara bring to the table, compared to similar tool like pynguin and Crosshair is that the analysis is entirely static, meaning that no user code will be executed, and you can easily extend the test generation strategy via plugin loading (e.g. the options arg to the Component object returned from function above is not needed for test coverage).

Automated White-box test generation software: * https://github.com/EMResearch/EvoMaster -- for integration tests. * https://github.com/se2p/pynguin, https://pynguin.readthedocs.io/en/latest/user/quickstart.html -- unit test generation for python

Pynguin – Generate Python unit tests automatically\ (60 comments)

This is a new stiff ODE solver which uses multi-threading within the method to make the solve of single ODEs faster in cases where it's too small to benefit from implicit threading in BLAS. Uses Polyester mixed with a very specific adaptivity method to optimize the work calculation to hit this performance. And of note, it's the type of manual parallel algorithm that cannot use array-based parallelism offered by machine learning libraries, so it's an algorithm you kind of need to implement in C++ or... Julia! Was a fun multi-year project and am really glad to see it completed.

The Folds.jl [1] package mentioned in the article is very nicely written.

For another alternative to Julia's built-in `Threads.@threads` macro, folks may also be interested in checking out `@batch` from Polyester.jl [2] (formerly CheapThreads.jl), which features particularly low-overhead threading.

[1] https://github.com/JuliaFolds/Folds.jl

[2] https://github.com/JuliaSIMD/Polyester.jl