140: Testing in Scientific Research and Academia - Martin Héroux

Scientists learn programming as they need it. Some of them learn it in college, but even if they do, that's not their focus. It's not surprising that sharing the software used for scientific research and papers is spotty, at best. And what about testing? We'd hope that the software behind scientific research is tested. But why would we expect that? We're lucky if CS students get a class or two that even mentions automated tests. Why would we expect other scientists to just know how to test their code? Martin works in research and this discussion is about software and testing in scientific research and academia.Special Guest: Martin Héroux.Sponsored By:PyCharm Professional: Try PyCharm Pro for 4 months and learn how PyCharm will save you time. Promo Code: TESTANDCODE22Links:Python Testing with pytest: Simple, Rapid, Effective, and ScalableTest Driven Development: By ExampleMy reaction to "Is TDD Dead?" - Python TestingMartinHeroux/pliffy: Plotting differences with PythonPyBites Code ChallengesPython MorselsMartin Héroux (@martin_heroux) / TwitterScientifically Sound‪Martin Héroux‬ - ‪Google Scholar‬spike2py · PyPIpytest-mpl · PyPI