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?
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.
Links:
- Python Testing with pytest: Simple, Rapid, Effective, and Scalable
- Test Driven Development: By Example
- My reaction to "Is TDD Dead?" - Python Testing
- MartinHeroux/pliffy: Plotting differences with Python
- PyBites Code Challenges
- Python Morsels
- Martin Héroux (@martin_heroux) / Twitter
- Scientifically Sound
- Martin Héroux - Google Scholar
- spike2py · PyPI
- pytest-mpl · PyPI
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