algebra-driven-design VS pynguin

You might want to read on Algebra-Driven Design and Data Oriented Design. If you are greedy, both books were pirated.

I would like to do this more. At the moment I don't do formal verification. However, I often borrow methodology from the excellent book Algebra-Driven Design (text available on GitHub but support the author if you find it useful!) when designing systems. This means I define algebraic data types to describe the program and the laws that relate the different types, and use that to guide implementation in a language that doesn't support ADTs.

Their approach might be to express their business logic as a carefully selected collection of types and laws, as described in Algebra-Driven Design (the full text is available on GitHub, please support the author if you find it useful though). I recommend this book to everyone because, even if you don't use this approach to design your programs, it's an excellent way to think about problems and better understand the problem space.

If you have an interest in computer science, category theory is very useful there. Here's a good book on the topic (Algebra-Driven Design, by Sandy Maguire).

This reminds me of QuickSpec (different from QuickCheck) in Haskell. It takes Haskell code and finds the Mathematical laws that the code supports. It does this using a sort of smart random search. I learned about this as part of the Algebra Driven Design book.

Sorry probably worth pointing out that I'm not referring to an Algebra in the pure math sense. I'm more referring to specifying algebraic laws that an API would have to satisfy as talked about in this book https://leanpub.com/algebra-driven-design.

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)