Slashdot Mirror
'The Code Has Already Been Written'
theodp writes "John D. Cook points out there's a major divide between the way scientists and programmers view the software they write. Scientists see their software as a kind of exoskeleton, an extension of themselves. Programmers, on the other hand, see their software as something they will hand over to someone else, more like building a robot. To a scientist, the software soup's done when they get what they want out of it, while professional programmers give more thought to reproducibility, maintainability, and correctness. So what happens when the twain meet? 'The real tension,' says Cook, 'comes when a piece of research software is suddenly expected to be ready for production. The scientist will say 'the code has already been written' and can't imagine it would take much work, if any, to prepare the software for its new responsibilities. They don't understand how hard it is for an engineer to turn an exoskeleton into a self-sufficient robot.'"
The abstract in Slashdot is pretty much the whole text in the linked post. The other 3 paragraphs repeat the same idea.
To a scientist, their software is simply a tool, a means to an end. Their results and discoveries are what they really care about. When it comes to reproducing scientific results for verification, it is actually advantageous that another group not use existing software. Another research group using the same faulty software, with the same hidden bugs, will likely come to the same incorrect result.
Productization of software is a completely different exercise. You have to make your software work for a larger crowd on a plethora of devices. You actually have to consider how your software fits into the larger product lifecycle. The key difference here is that you have customers that you need to keep happy.
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You can often tell whether someone is "programming as a means to an end (of your own)" versus "programming to build a tool for someone else". For instance, I have experience in the financial industry. Quite a lot of traders see coding as a means to implement their cool new model. Looking at their code, you can often tell. It's as if everything was built to just exactly fulfil the requirement, with no thought to the fact that those requirements might change. But of course, they do change. So you get hacks and workarounds, and cut'n'paste cargo cult code. Kinda like what those Orks in Warhammer 40K might make. And of course the problem with spaghetti code is that if you write it, nobody can ever help you solve problems/improve it. It's the coding equivalent of painting yourself into a corner. There's loads of smart traders out there with an excel spreadsheet that actually is an extension of their personalities (In fact it's their Magnum Opus. Everywhere they go, they try to take this quirky little file with them). Every little hack is something only they can explain (comments, yeah right. Do your body parts have explanatory comments?) and only they can fix if wrong.
On the other hand, you sometimes hire a guy who is a programmer, but knows nothing about the domain. Very good with OO models and that kind, but you have to teach them everything about finance. What's a settlement date, what kinds of options exist, etc. You get what you ask for, because they know how to turn problems into object models, but you have to ask VERY carefully. And teach. Unfortunately, not everyone has time for that, and so you end up with something that still doesn't quite do what it's supposed to.
So you often end up gettings guys who understand the problems, but can't program, programming. And guys who can program, writing the wrong program.
The issues surrounding transitioning research S/W written by scientists into honest-to-goodness production systems are ones I'm very familiar with.
At my company, a lot of energy has been put into bridging the gap over the years with varying results. I believe that the root cause of the problem is that research S/W is not an end-product; typically for scientists the end-product is a research paper, white paper, proposal paper, etc., for which the S/W is only a tool for getting to the end-product. As soon as the experimental (or proof-of-concept) S/W returns the desired results, the software is considered "done".
In contrast, production S/W is often THE end-product for developers, so a lot more attention is given to robustness, re-usability, etc. All the standard thinking that you want to go into your production S/W.
One big issue for us is that the research S/W is almost always written in Matlab, while the production code is written in C++ and Java. The single largest source of bugs in our systems is porting S/W from Matlab to C++ or Java. (As an aside, please let's not talk about the Matlab 'compiler', nor Octave. -- we've already tried them both, and they're both performance hogs and also create SCM and CM nightmares).
We experimented with requiring that the research S/W be written in C++, but it was a disaster. The scientists couldn't get anything done, and the code was just awful. So, back to Matlab it was.
And, my experience is that people who I have a great deal of respect for, who I consider brilliant in their fields, holding PhD's, etc., have produced the crappiest Matlab code I've ever had the sorrow to read. My favorite instance was the use of these local variable names within a single function of research S/W that was considered "done" (true story):
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And, of course, little documentation as to the mechanics of the code. And believe me, it gets worse from there. Bear in mind that the code does indeed work for its particular purpose, and may well be ground-breaking in that particular research domain. But "done"? Ready for production? Not without a major porting effort (which is really a re-writing effort). The most mysterious thing to me, though, is that the scientists, for all their intellectual firepower, don't understand that it's a problem.
The solution we've converged on is to require our bizdev to be responsible for funding efforts to rewrite the research code and get it integrated into the product baseline. And, the bizdev types can't proclaim a particular capability "done" (eg., sell it to customers) until they've funded and executed those efforts. It took years of education to get to this point, but things are moving along much better then before.
In the course of every project, it will become necessary to shoot the scientists and begin production.
"It definitely is functional but hardly has any of the features consumers demand."
isn't this expected (or at least not surprising). if I were a NASA engineer, I'd see the program as a tool to help me accomplish the larger task. the more time I spend on tools, the less time I spend on progress to the larger objective. I'd write the program as quickly as I could.. would not care about UI, functionality, usability or anything else, I built it for me to use - as long as the output satisfies my needs, i'd consider the task done and move on.
You seem to overlook the fact that computer programmers usually have tight schedule and budget constraints enforced by their supervisor or other management they report to, instead of by the customer (the scientist in this case). Get a computer programmer a gig as sweet as a scientist who can take his or her sweet time to do their research and give the programmer that same open ended time frame with decent equipment and no schedule constraints and you would have a much happier, involved, and more responsive computer programmer.
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That's a great post, of the kind that saves me a lot of typing. You covered the first-order considerations brilliantly.
What you missed was technical debt blindness, which has been around since forever. Books I read around the time of the Mythical Man Month talked a lot about maintenance syndrome: that the original development team would be regarded as brilliant for producing working functionality at tremendous speed (undocumented, with no error handling for edge cases), then the first maintenance team would all be fired as underachievers for adding hardly any new functionality in the first year or two.
Turns out it's hard to erect a machine shop over top of adobe mud brick construction without adding some reinforcement to the structure, which usually takes a lot longer than the entire original edifice.
You can instead take a wrecking ball to the first iteration, but this rarely works out as well as hoped. You end up with far more ambitious adobe mud construction built with a whole new generation of unproven tools. At some point you have to bite the bullet and ferment what you began with.
People hide debt blindness behind widely divergent construals of simplicity, where "simple" usually turns out to be a euphemism for any decision that sidesteps paying down debt in the short term.
For professional software engineers, there is one true simplicity to rule them all: generativity and compositionality. Can you build the next layer on top with any hope of having it work and able to support an ongoing stack? For us, it's a long term game of pass the baton. For everyone else (management, scientists) the endgame is to cash out, and take credit elsewhere (e.g. publication biography).
Unfortunately, a citation is not a formal linkage that the compiler either accepts or rejects. By the standards of compositionality, citation is payment in dubious coin. Citation is not falsifiable. Scientists still count their citations even when they come from papers that are full of crap, peer review notwithstanding. For a professional software engineer, when you start instantiating objects from one library inside an abstract expression template library, you come face to face with compositionality in a way that few scientists can even imagine, having weened at the outrage of being improperly cited.
Technical debt blindness on the part of management quickly turns a software engineering shop into a highly non-linear fiasco. We've all seen this.
Somehow this game works out better (for the participants) when played by bankers with leverage debt. But now it's my turn to pass the baton, since that deserves a whole lot more typing and I've done my bit.
I work for a group at NASA. One of our group's tasks is to take scientist-written code and wrap it for distribution to hundreds of remote sites around the world. We try our damnedest to run the code as-is, but fairly often have to modify it to remove stuff like:
* Hard-coded input and output file and directory names
* Small and arbitrary length limitations on file pathnames - I've run into buffers that were declared as 53 characters in length, probably because that was what they needed on their system
* Large arrays being allocated on the stack - Linux distros have different default stack sizes
Most of the problems stem from the picky crap that C makes users go through for simple stuff like string manipulation. Both the scientists and the downstream developers like me would be MUCH happier if the scientists worked in something more forgiving (Matlab/IDL/Mathematica, or a flexible scripting language with a decent interface to heavy duty math libraries).
To a Lisp hacker, XML is S-expressions in drag.