I work in science. We agree that testing would be beneficial, but nobody codes well enough to actually get it done.
To all language designers, there is a HUGE space for a better scientific language. Make it easy for Matlab users to understand, but include better encapsulation and library support. Tie in testing and proving from the core.
You might want to check out Julia[1] programming language. I have absolutely no idea how good or bad is it but considering the fact that it's so young -- you are still able to influence it's development (e.g. suggest better testing capabilities) if you really wanted to.
The webpage is down, so I'll take a look at it later. I'd like to see some simple examples of things as well.
It should be a one-liner to import a CSV file and do a least squares regression on different columns.
It should also be a one liner to open an image, compute it's 2d FFT, and display it.
It should also be one-liners to do numerical quadrature integration, compute the solutions to some ODEs, and maybe even backpropagation training of a neural network with just one layer.
NumPy is making pretty strong headway in many communities. It's still not Matlabby enough for the majority, though. It doesn't make a clear improvement, so I think many see it it as just poorly replicating the features of Matlab for free.
It's also pretty notoriously difficult to install, especially if you want LAPACK/BLAS. I wasn't able to get it running on many of our servers for that reason and had to revert to Matlab.
To all language designers, there is a HUGE space for a better scientific language. Make it easy for Matlab users to understand, but include better encapsulation and library support. Tie in testing and proving from the core.