Author’s Note: Having refrained from actually posting this for 8 months, as I think I have quite mellowed more my critique. I do believe that education and peer review are the best way forward in tackling these issues, but cannot overstate how much of a long and rocky road that process will be. The Mozilla Science Foundation is really leading these efforts with their code review pilots (as I have discussed in posts since writing this), and through their work with Software Carpentry training. Still, I leave this post as a bookmark of my intial thoughts and a reminder of these challenges.
Consider reading these other posts on software review and training:
- Reflections on the Mozilla Code Review Pilot (phase 1)
- ISEES Meeting software lifecycle: Day 1, Day 2
- Reviewing Software, revisited
I have just been reading through
r citet("10.1126/science.1231535", "critiques"). While I agree with a lot of the sentiment in this article, (we place way too little emphasis on good coding practices, validation, etc.), I actually found this article incredibly frustrating. Following a little rant on Google+ (G+ has to be good for something, right?) and subsequent discussion, I’ve tried formulating my thoughts into a blog post here.
What first got my goat were the proposed “solutions” to the problem. I think it is hopelessly naive to write that the solution is “better computational education” and “peer review of code”.
Calling for better education is a cop-out
I completely agree that better education is sorely needed, and am delighted to see the authors cite the Sofware Carpentry project (Though couldn’t they have made this a proper citations with a link?). However, this is an all too common cop-out of an answer that will do little to address the real problem. For decades such position papers have called for better education to address all our weaknesses – biologists should learn better mathematics, better statistics, better programming skills. They should also learn proper data management and archiving skills no doubt. While we’re at it, they should learn more about communicating science and public speaking too. What these calls to “raise standards” have in common is a dearth of incentives. Reward these talents with jobs and advancement (publications and grants, after all, are just a means to such ends, are they not?) and the education will follow. Economics teaches us that if we want to change behavior, we change incentives. Unless this education translates into the currency of academia, we will only do our students a disservice by forcing it upon them. (I do note that Software Carpentry rightly claims that the kind of training it offers does promise to pay off in the current currency through time savings that will be realized down the line…)
Peer review of code is not the answer
If “peer review” were the ultimate solution to good code, we’d see that standard in the software industry too, wouldn’t we? Peer review actually is used in the software industry, if perhaps more the way we treat “friendly review” then with the black-and-white view we attach to peer review in the scientific literature. Peer review would never guarantee the validity of code, though it would certainly help. The real bugbear with peer review of code is just how difficult it would be to establish as a practice. The authors are silent on these challenges: organizing, incentivising, transitioning to, and paying for such a system would all be major hurdles to overcome.
I would trust software that has a long and active development history with an engaged user base much more than anything that has simply been “peer reviewed”. I suspect that instituting peer review of code in the sciences would be a huge challenge for a potentially limited payoff. If open source taught us nothing else, it is that “with many eyes all bugs are shallow”. Robust software must ultimately rely on bug report feedback cycle of users (and developers following the “dog food” rule).
So what is the problem, exactly?
If the proposed solutions are weak, the statement of the problem is not actually much more convincing. What, exactly, is the “troubling trend” alluded to in the title? The primary concern from their survey appears to be that scientists rely on personal recommendations, ease of use, and frequency of use in published studies when choosing what software to use. This approach is only “dangerous” if we assume that a scientist has a choice of N potential software applications to perform a task, of which some fraction F are faulty. (And that a “worrying” 80% want to become better programmers…)
But does this study argue any of the software there users approached was faulty? No. Do they provide evidence that the metrics a researcher uses (popularity, trust, ease of use) are not good predictors of decent software? No! They just assume this can’t possibly be a good criterion to evaluate software.
Suggesting that this criterion is to blame misses the underlying problem entirely. Researchers will always prefer software that has been used in published studies, is easy to use, and recommended by people they trust. If this is leading to problems, we must fix the software, not the people.
Reliance on common software is replacing reliance on undisclosed software written from scratch by each researcher. It is much easier to identify and correct errors when the community all uses a common piece of software then when everything is done from scratch. Crucially, this shows the emergence of a common code base – a shared software infrastructure, emerging in many fields. This development is to be celebrated and taken advantage of rather than something to fret over. A shared infrastructure is a powerful thing.
Real solutions: changing incentives
Of course there are some real gems in the paper that should have recieved more emphasis.
Current models for how scientists and journals are rewarded must change, as the would-be editors of the Open Research Computation journal (now a series of the journal Source Code for Biology and Medicine) discovered during efforts to establish a journal for publishing peer-reviewed software ( 27).
Reward software the way we reward papers. Github model of contributions.