strace is one way to determine how many stat calls a process makes. Developers avoid refactoring costs by using dependency inversion, fixtures and functional

strace is one way to determine how many stat calls a process makes. Developers avoid refactoring costs by using dependency inversion, fixtures and functional test assertions without OO in the tests, too. Pytest collection could be made faster with ripgrep and does it even need AST? A thread here mentions how it's possible to prepare a list of .py test files containing functions that start with "test_" to pass to the `pytest -k` option; for example with ripgrep. One day I did too much work refactoring tests to minimize maintenance burden and wrote myself a functional test runner that captures AssertionErrors and outputs with stdlib only. It's possible to use unittest.TestCase() assertion methods functionally: assert 0 == 1 # AssertionError import unittest test = unittest.TestCase() test.assertEqual(0, 1) # AssertionError: 0 != 1 unittest.TestCase assertion methods have default error messages, but the `assert` keyword does not. In order to support one file stdlib-only modules, I have mocked pytest.mark.parametrize a number of times. chmp/ipytest is one way to transform `assert a == b` to `assertEqual(a,b)` like Pytest in Jupyter notebooks. Python continues to top language use and popularity benchmarks. Python is not a formally specified language, mostly does not have constant time operations (or documented complexity in docstring attrs), has a stackless variant, supported asynchronous coroutines natively before C++, now has some tail-call optimization in 3.14, now has nogil mode, and is GPU accelerated in many different ways. How best could they scan for API tokens committed to public repos?