Thu 18 October 2012 In science. tags: sciencereplication

After yet another round of futile Twittering on the subject of research software, I thought I'd share a deeply personal story -- a story that explains some of my rather adamant stance that most research scientists need to think more critically about their code, and should adopt at least some of the basic coding hygiene used by virtually every modern practicing programmer.

A painful personal anecdote Way back in the early 2000s, I switched over to Python as my day-to-day working language. I was in my graduate lab doing bioinformatics and genomics, among other things, and Python was rocking my world. I'd passed through several previous languages for research work -- C, Tcl (anyone else remember ArsDigita and AOLServer?), and Perl -- but Python was so readable and easy to modularize that I found myself actually reusing substantial amounts of code by sharing it between projects. An awful lot of the time, this shared code was in the form of scripts, little snippets of code that read in sequences, did horrible things to them, and spat them back out. Or correlated lots of different BLAST files. Or whatever needed to be done. Now, I wasn't yet storing these scripts in version control -- centralized version control is hard to justify for lots o' little disconnected scripts, and that's all we had in the bad old days -- but I was reusing them. And just about every time I used a script for more than one project, I found a bug or two. Not often a big one, since I was a reasonably careful programmer and I was doing very simple things, but almost always something. And, of course, the longer the script, the more likely I was to find that some portion of it was buggy. But even in my smallish 15-30 line scripts, I would find off-by-one errors, or a misplaced 'if' that lacked an important 'else' for some corner case. Being a scientist, it was hard to escape the implication that most of my scripts were buggy, not just the ones I ended up reusing. Did I start doing any kind of automated testing of my scripts? Hell no! Anyone who wants to write automated tests for all their little scriptlets is, frankly, insane. But this was one of the two catalysts that made me personally own up to the idea that most of my code was probably somewhat wrong. It also led to this aphorism: Every time you write a script, God kills a kitten. (Please, think of the kittens!) This encapsulates the idea that yes, you've gotta write scripts -- and some kittens are gonna die -- but you should try to minimize it. (See my second postscript for more on my approaches to scripting.) So that's why I started thinking about code reliability: because my code was, demonstrably, not reliable.

On good practice Now, over the years, I've had many, many discussions with scientists about their code. Yes, most scientists aren't formally trained in anything approximating software engineering (note that it turns out that many software engineers were never formally trained in this either -- our undergrad CS education is 5-15 years behind the times). And yes, I think a lot of the standard software engineering stuff, including things like patterns and object-oriented design, is serious overkill for most scientists. But I've never been impressed with any of the multitude of reasons given for avoiding certain kinds of good programming practice. I think there are three particularly important points of practice that we hit on in the Best practices for Scientific Computing paper: First, use version control. Traceability and provenance is important; we expect experimental scientists to keep a lab notebook, and you should view version control as a computational equivalent for your source code. (As I told one postdoc here at MSU, if you aren't using version control for your code, stop pretending to be a scientist. I make myself popular, I do!) Second, optimize later. I write all my code in Python first, and organize my code and algorithms in Python. I then build in C and C++ sparingly and as needed. This strategy lets me think about function and correctness in a language that's succinct, easy to write, easy to organize, and easy to read -- and it works because Python integrates so nicely with C and C++, so I can slowly refactor speed and memory improvements in over time without affecting the core Python functionality. Third, plan for mistakes! You will screw up, and the important thing is not to prevent it in the first place, which is impossible, but to plan around catching them.

What kind of testing should you do? I highly recommend three core testing approaches. The first is what I (improperly) call regression tests: these are tests that verify that, on certain test input, code is performing the same today as it did yesterday. This corresponds to the basic principle that if your program's observed behavior changes unexpectedly, you should probably figure out why. The second testing approach is Stupidity Driven Testing, in which you write tests for bugs that you actually find. The basic principle here is that once you've found a bug, you should probably make sure you avoid making that same mistake again. (This also has the salubrious effect of adaptively targeting tests at the bits of code that fail most often!) The third testing approach I recommend is to use code coverage analysis to drive the writing of new tests. Code coverage analysis (at the statement level, for you purists) simply tracks which lines of code are executed during your existing tests, and for mature code your goal should be to reach 80-95% code coverage with your test suite. Argumentative people are always quick to point out that just because code is executed by a test doesn't mean that it's correct, which is true; my response is that if code isn't executed by any test whatsoever, then we know it's not being tested. Prioritize, people! There are all sorts of other things that people recommend doing, but I've never been a fan of things like Test Driven Development. shrug If you believe strongly in it, write a blog post telling me why I'm wrong :)

Myths of research software Let's do some role playing. You, dear reader, are an overwhelmed and (in the area of programming practice) a largely uneducated graduate student/postdoc/faculty member. You are desperately trying to avoid the extra work I suggest above, and are convinced that research software is somehow different from all other software. And I? I am an overly experienced, frustrated, and angry man pointing out that you are wrong :). Go! Most research software is only run once, or a few times! Why put in the effort to write tests? There are a few problems with this one. First, you should still care if you get the correct answer, right? And second, I think one implication here is that you don't particularly care about maintainability of research software and shouldn't expend the extra effort. But the problem is that maintainability and correctness have an obvious and intuitive link: programs that are easy to maintain are easier to understand, and programs that are easier to understand are much more likely to be correct. More, I don't know of any studies showing that cowboy coding (write code! trust results!) results in reliably correct code, while I can definitely point you at studies that show that some kind of good practice, including any of formal design, code review, and automated testing, leads to more reliable code. There is a third problem here, too: successful code often will be reused, either by you, or by your labmates, or by your readers. (You are publishing your code with the paper, right?) You'd be surprised how often I've needed to dig into old code to repurpose it... People should be *rewriting* my methods, not *reusing* them OK, that is defensible from a purely scientific point of view (see Accountable research software). But, as Victoria Stodden pointed out to me in a private response, how do we track down the source of discrepancies between two implementations of methods? What, reimplement it a third time? And, more generally, this whole rewrite-and-don't-reuse schtick sounds like a gigantic waste of time to me. I'm sympathetic to the idea but ultimately think we have better things to do, like worry about whether or not the results from running the program are scientifically useful and correct. Good industry practice doesn't fit with research You can always come up with a laundry list of rather nonspecific things, like "I'm doing stochastic simulations; test that, biatch!" (Sure -- that's what regression testing and pseudo-random number seeds are for.) Or "version control doesn't work for storing multi-gb data sets." (OK, don't store them there, then. Just put the code in version control, like everyone else.) Or "how do I write unit tests when I don't know the answer?" (We should probably talk about what you think unit tests actually are, first.) Or "github doesn't solve all my problems, so I'm not going to use it." (Uhhh... ok, how about using it to solve some of your problems?) Hey, I get you. It's not easy. And you've never been trained in it, either. But you're smart. If you actually care about whether or not your code is correct, figure it out. (We're Software Carpentry. We're here to help.) I think this notion that research software is something special and deserving of some accomodation is so wrong that it's hard to even address it intelligently. What, you think people at Google aren't doing exploratory programming where they don't know the answer already? You think Amazon customers don't behave in unexpected ways? You think Facebook social network data mining is easy? The difference there is that companies have a direct economic incentive to solve these problems, and you don't. More, I have the niggling little feeling that this argument is frequently trotted out by people who want to be lazy. I'm actually completely on board with the idea that you find everything I have to say about actual practice to be useless -- and I'd love to find out why I'm wrong, and understand how what you're doing is better! But when you say "it's just hopelessly different! I give up!" I am suspicious of your motivations... My boss doesn't care. Yeah, that's a big problem. They're wrong. Become your own boss :). I don't have time to do all this stuff Sure, time is my big problem too. I just have lots of bad experience that suggests that most of my code is buggy in one way or another, and that motivates me to do something about it. More generally, are you feeling lucky, punk? You are statistically unlikely to be forced to retract your work due to a software bug (although see the list in the third paragraph of our Best Practices paper). And you're not in a company, where a bug can cause your company or your customers real pain. No, you're just involved in mankind's greatest endeavor, trying to understand the universe, solve pressing societal problems, cure diseases, and provide a better tomorrow for Amarie, Jessie, and Maddie. But that's ok -- it's not all on your shoulders. Just a little bit.

Ignorance is not an excuse Science in general, and biology and bioinformatics in particular, are suffering from the snail's pace at which education changes. We simply don't train people in this stuff, which is why efforts like Software Carpentry are so #!#%!% important. But, at the end of the day, it's not ok to be a computational scientist and ignorant of good practice in programming any more, just like you can't do data analysis and be completely ignorant of statistics, even if you have no formal training. As a researcher it's your responsibility to do a good job on your research, period. If that means learnng something new, well, you've presumably had to do it before, and you'll have to do it again!