How to test scientific software?

Question

I'm convinced that software testing indeed is very important, especially in science. However, over the last 6 years, I never have come across any scientific software project which was under regular tests (and most of them were not even version controlled).

Now I'm wondering how you deal with software tests for scientific codes (numerical computations).

From my point of view, standard unit tests often miss the point, since there is no exact result, so using assert(a == b) might prove a bit difficult due to "normal" numerical errors.

So I'm looking forward to reading your thoughts about this.

Answer 1

Just been looking at a similar issue (google: "testing scientific software") and came up with a few papers that may be of interest. These cover both the mundane coding errors and the bigger issues of knowing if the result is even right (depth of the Earth's mantle?)

http://http.icsi.berkeley.edu/ftp/pub/speech/papers/wikipapers/cox_harris_testing_numerical_software.pdf

http://www.cs.ua.edu/~SECSE09/Presentations/09_Hook.pdf (broken link; new link is http://www.se4science.org/workshops/secse09/Presentations/09_Hook.pdf)

http://www.associationforsoftwaretesting.org/?dl_name=DianeKellyRebeccaSanders_TheChallengeOfTestingScientificSoftware_paper.pdf

I thought the idea of mutation testing described in 09_Hook.pdf (see also matmute.sourceforge.net) is particularly interesting as it mimics the simple mistakes we all make. The hardest part is to learn to use statistical analysis for confidence levels, rather than single pass code reviews (man or machine).

The problem is not new. I'm sure I have an original copy of "How accurate is scientific software?" by Hatton et al Oct 1994, that even then showed how different implementations of the same theories (as algorithms) diverged rather rapidly (It's also ref 8 in Kelly & Sanders paper)

--- (Oct 2019) More recently Testing Scientific Software: A Systematic Literature Review

Answer 2

I am also in academia and I have written quantum mechanical simulation programs to be executed on our cluster. I made the same observation regarding testing or even version control. I was even worse: in my case I am using a C++ library for my simulations and the code I got from others was pure spaghetti code, no inheritance, not even functions.

I rewrote it and I also implemented some unit testing. You are correct that you have to deal with the numerical precision, which can be different depending on the architecture you are running on. Nevertheless, unit testing is possible, as long as you are taking these numerical rounding errors into account. Your result should not depend on the rounding of the numerical values, otherwise you would have a different problem with the robustness of your algorithm.

So, to conclude, I use unit testing for my scientific programs, and it really makes one more confident about the results, especially with regards to publishing the data in the end.