Too much of the research process is now shrouded by the opaque use of computers

Some Scientists are used to describing something by stripping away all the irrelevant matrix and concisely stating the idea at issue. Maybe they write code that way - as little boilerplate as possible, just a few concise lines that capture the algorithm.

Or maybe they write hash, I don't know. But their approach isn't 'let me craft some good code', its 'let me describe my thesis' which will end up in a different place than any trained software professional.

All generalities are unfair to some extent, it's just how generalities work, so I tend to read them as a quantified statement based on the number of people in the group that the person making the statement is likely to have interacted with. In this case, it seems as if the poster works with scientists on a regular basis, and that is his experience with those he has worked with. That being said, I'll pile on some of my own anecdata in just saying that in school I was split between physics and computer science and my initial approaches towards software development were certainly influenced by some of the thought patterns that the poster was expressing. I've come around on a lot of things, but software developers definitely have a different mindset about software because we develop it.

Yes, I'm also aware that I made a general statement about generalities, though I think this one is a mostly correct generality.

It's bizarre to me how byzantine the scientific community's grasp of programming is. Variables are named things like "namxaqqs" and "stoggs1_r3". 4,000 line monster functions are rampant. If scientific code were a garden, stuff like this would be the weeds:

    Ax_1 += Bx_1 + Cx_1 # Add Bx_1 to Cx_1
    Ax_2 += Bx_2 + Cx_2 # Same thing, but for Ax_2
    Ax_3 += Bx_3 + Cx_3 # "
    Ay_1 += By_1 + Cy_1 # "
    ...

Code with comparable functionality is copied and pasted instead of being factored into a single function. Everything is tightly-coupled: changing one line of code is like pulling the keystone out of a bridge designed by an oyster chef. If there are any functions, then calling one mutates at least 7 global variables and induces 13 side effects that are more unpredictable than eigenstate selection. File formats are non-standard and consist largely of one giant concatenation of every variable in the program (all converted to strings of course).

Not-invented-here-syndrome is a badge of honor (LAPACK? Bah! I'll write my own Gaussian elimination routine for this matrix with a million entries). Libraries are embraced with the exuberance of a picky eater encountering durian (as a rule of thumb, anything that is open source and has been vetted by thousands of users is probably untrustworthy).

It is considered a waste of time to learn basic CS algorithms — efficiency is merely an implementation detail, so problems that could have been solved with a clever algorithm and an iPhone are instead brute-forced using millions of hours of supercomputer time. Complexity classes are the abstract nonsense of computer science — it's much easier to throw more hours at the problem (so what if it's NP-hard? My algorithm probably converges to the global minimum. Why wouldn't it?)

When garbage-collected languages are used, programs spend 99% of their time allocating and deallocating small quantities of memory in tightly nested inner loops (16 of them, no less). "Inlining" means putting comments inside of the code instead of above it (if there even are any comments). "Cache locality" has something to do with GPS systems. "Hashing", "recursion", and "quicksort" are the names of recently-announced smartphones. And doesn't "SIMD" stand for the Society for Inherited Metabolic Disorders?

/rant

It's a mess. Granted, there are researchers who write very high quality code, but they are few and far between. I think the main problem is that a lot of students/professors who get involved in computational research never had a good CS background. Perhaps they had one "Computing for Engineers" class that taught Matlab or Python, but that isn't nearly sufficient for research-quality code. While I had the advantage of taking up programming as a hobby during childhood, most graduate students have never programmed before in their life. They're expected to learn something like C++ in a week (that's not a hypothetical example).

Many researchers are afraid to publish their code alongside their paper because they know the code is low-quality. And that awareness causes a lot of insecurities. If someone finds a bug in their 2-3 year research project, then their entire conclusion might be invalidated. But I don't think that's the biggest worry (most scientists ultimately want to know the truth about their subject of study). I think the biggest fear is of losing prestige, losing a chance for tenure, or having funding revoked.

To fix this problem, there is a crucial and urgent need for the academic community to reduce the penalty associated with making honest mistakes.

Mistakes are simply part of the research process. Humans are fallible — everyone is going to mess up at some point. And instead of propagating this academic "chilling effect", it would be much better for the whole scientific community if everyone quit worrying about messing up and instead published their code in a highly visible location, subjecting it to the highly critical (yet extremely beneficial) scrutiny that it deserves.

A project just launched yesterday is Depsy [1], from Impact Story. Depsy tries to give credit to the coders who write scientific software that's used in research. One element at play is that it's not worth it from a reputational standpoint to go through the hard work of getting your code ready to release publicly. It's the same problem we all have with open sourcing hacked together side projects (except even relatively shoddy side projects do help build a programmer's reputation). Depsy wouldn't totally solve that problem, but if there was a good way to boost your reputation by contributing public code then you might see changes in the industry. But like all things in academia, you have to start with reputation and work your way to a solution from there.

[1] http://depsy.org/ and the blog post announcing it: http://blog.impactstory.org/introducing-depsy/

Well to give an example: good luck finding an Ada compiler that compiles to PDP-11, and then good luck getting it to run :)

And this is even an example of a combination which once was considered mainstream.