pynguin VS methods2test

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)

Datasets: * https://github.com/microsoft/methods2test -- dataset of Java code and unit tests -- so one may used supervised learning to generate tests. Similar dataset may be available at your company for ABAP, and you may try training an encoder-decoder / transformer model for test case generation.