Scientific software engineering, Jan 2017 version

As part of my Moore Foundation Data Driven Discovery grant, I have to put together annual reports each year. (This is more or less standard for grants. ;) You can read my annual report narrative, here, and my (ancillary, not required) breakdown of projects in the lab, here.

Software development is hard

In the project breakdown, I discussed our software development efforts since the move to UC Davis and Michael Crusoe's departure. I say,

khmer remains central to the lab and, a well as a suite of useful exploratory tools, is now maturing into a robust platform that several projects in the lab are relying on. Despite this I am still somewhat alarmed (at a strategic level) regarding the level of resources required to achieve even a minimally responsible level of scientific software development.

A bit further on, I make the point,

...we continue to find minor and not-so-minor bugs in khmer. This suggests to me that plain ol' software bugs are likely to have a significant contribution to the repeatability/replication/reproducibility crisis.

This point isn't new -- see Soergel, 2015, "Rampant software errors may undermine scientific results" -- but I wanted to mention it again :).

Interestingly, we find the major value of our "exhaustive (and exhausting)" testing regime to be less in finding new bugs, and more in allowing us to fearlessly refactor and adapt the code base to new projects as needed. Daniel Standage and I have had several discussions on this about khmer, as he was new to the experience; and I'm watching it work again in the sourmash project as well. (I should say that this is all probably obvious to industry folk, but it seems not to be obvious to academics!)

In sum, even for a little bitsy project like khmer, sustained software development is hard. We're taking the attitude that correctness is critical, but I have a lot of sympathy with folk who conclude out of expediency that maybe it's too hard for their project. See: Please destroy this software after publication. kthxbye.

Contracting out bits of software development may be an effective strategy

After (literally) years of trying to figure out how to move software engineering forward in a sustainable way, I've reached a solution that I'm happy with. The solution:

  1. Run the project as an open source project, with the testing, and the code review, and the issue tracker.
  2. Contract out to someone from the open source community to help with code reviews, optimization, and issue whacking.
  3. Task people within the lab with leading their scientific efforts and integrating it into the software as they go.

In this case I've got Daniel to keep an overall eye on the science aspects of the project, and I've contracted with Tim Head (@betatim) to do open source-y stuff.

There's a lot that goes into this, but I'll say that in the past I've had a lot of trouble balancing the software engineering against progressing the science. One of the mismatches - especially for a small lab like mine - was between the effort a full-time software engineer could put in, and what grad students and postdocs found possible given their projects. This led to lopsided investment in software engeering vs research. The tension remains, of course, but it's much reduced.

A few points --

  • "For our lab, a ratio of 1 half-time software engineer to 2-3 active grad/postdoc developers seems to work ok."

  • I think this can scale in either direction. I promised Tim a minimum number of hours over a 3 month period, but can reduce or increase that as needed over longer periods. And, if we suddenly get an influx of money or project-related work, it seems like there are a fair number of PhD-level open source hackers out there that are available for hire.

    If I can ever do all the paperwork, I'd be happy to hire current grad students and postdocs as contractors, too. (I was about halfway down that road with Upwork when Tim came along, and the paperwork was much easier this way :).)

  • I'd like to think I'm paying Tim a reasonable amount (although I'm definitely not hiring him at Silicon Valley rates). In return, he gets super-flexible working hours, the ability to put the work on his C.V. as part of an open portfolio, some occasionally interesting problems, and interaction with a new problem domain (he's a physicist by training, and while they are automatically expert at everything, there are niggling details about biology that he's still learning).

  • It is very useful to run this project like a community-based open source project, even if we have relatively little in the way out regular outside contributors; it means that people can come and go, and catch up on project history, and so on, and it enables asynchronous work in an excellent way. (We're not nearly at dat scale async but I'm heading in that direction. :)

Refactoring vs software architecture redesign: Small moves, Ellie.

About every 6 months to a year, the question of investing in real software design comes up. Yesterday, Daniel jumped in..

It's clear from my response on that issue that I'm gunshy about large scale architecture redesign :).

On the flip side, small scale changes and incremental refactoring are messy, confusing, and slow; our latest effort to consolidate and de-obfuscate our sequence reading code is a tangled mess of issues that is almost impossible to understand. I have to sit down and reset my brain and read the issues for 10 minutes before each comment. (SOON THIS DRAGON WILL BE SLAIN, however.)

Flipping the flip, I'm pretty happy with how the last big code reorganization effort turned out. And the current effort is revealing some lapses in our test suite - the last line of that comment is me saying "oh shit! that shouldn't have worked!" - and moreover this is an area where we've fixed some bugs and identified some others so I think continued attention and test suite expansion is warranted.

And for a last reversal, I'll just say that our current collection of k-mer processing code is a crazy nightmare of functionality that is only barely kept under control by our testing infrastructure. (THIS DRAGON SLEEPS, ALBEIT FITFULLY.)


  • there may be a place for rethinking architecture, but I've never seen large scale architecture change work out well in practice. See: Perl 6.
  • small scale changes yield messy interim code but seem to wind up yielding ok results. See: Python 3.
  • this really needs to be a constant effort on code bases; your code base is either "living" or "dead", and living code needs constant maintenance. But maybe it beats the alternative?


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