We’ve all read about the huge rise in unemployment claims causing unprecedented loads on US state software systems, with the situation was so dire that the governor of New Jersey put out an urgent call for COBOL programmers. It’s worth looking at this form the point of view of research software, where we need software to be sustainable and reproducible for long periods of time.
The systems that need suddenly need COBOL developers have often been chugging away with maintenance and tweaks for 40–50 years. This is an almost unfathomable success in the world of software. So the current issue clearly isn’t with the quality of the software itself per se.
Is COBOL being “obsolete” the problem? I mean, look at that record of success again. COBOL is proven, perfectly serviceable, domain-specific language for these sorts of batch tasks. There’s ways to connect to tools and services written in other languages, so it can coexist with other systems. The lack of (say) a vibrant and rapidly-evolving ecosystem of third-party packages isn’t necessarily a bad thing here. (How innovative and cutting-edge do you want the system that sends out your pension cheques to be, exactly, when the time comes? Do you really want someone to accidentally leftpad your bank account?)
Yes, people coming in to maintain the software for the first time will have to familiarize themselves with a new, old, language. But people in research or open-source software learn an unfamiliar language to contribute to a code base every day. Even if they knew the language, they would still have to learn the codebase itself, the idioms, and the problem domain. All of those things can be quickly learned by new developers if there is documentation and tests, and especially if there are people who have recently been maintaining the code base to help. And that’s the issue here.
These COBOL systems weren’t poorly designed, or obsolete, or a bad match to their requirements. Easily handling 100x the previously expected maximum rate of applications isn’t a feature, it’s a symptom of giddy overengineering. The requirements just changed suddenly. And when that happened, the people, procedures, and resources weren’t in place to do the necessary maintenance.
There is no such thing as infrastructure which does not require maintenance, and the need for that maintenance is often quite bursty. This is just as true in research software as it is in governmental systems. Research software which goes into production needs to be written in a maintainable fashion, but that’s not enough. There has to be funding support to keep in place the people, procedures, and resources necessary to maintain that software, likely in bursts. And those resources have to remain in place between bursts.
The bursty nature of necessary maintenance has also come up in research software, in the saga of the Imperial College epidemic modelling software. When COVID-19 arrived, this tool suddenly moved from a mildly interesting research code to a key input into UK domestic policy. Transparency and flexibility leapt from being nice-to-haves to key requirements, and the people, procedures, documentation, tests, and resources weren’t in place to add them.
The importance and urgency of epidemic modelling meant that expertise and resources from many places were made available to extend and eventually rewrite the code. But this isn’t a sustainable model for research computing software, any more than it is for unemployment application processing systems.
We still genuinely don’t know how to reliably provide maintenance, bursty or otherwise, for software and shared databases or systems in our research communities. Our funding models are all built around supporting experiments, observations, or theoretical works — short-term projects which start, proceed, result in publication(s), and other research outpus, and are then done. Mechanisms for ongoing support of evolving research inputs isn’t even a work in progress — it’s absent.
If experimental methods work develops new kinds of equipment or reagents which are useful to other researchers, then a vendor starts manufacturing and selling those items to researchers, with money that comes out of their grants — and that’s the sustainability model. We don’t have that for ongoing efforts in software, databases, or even reliably for hardware shared at a larger scale than a single department yet.
For software undergoing active development, there are at least plausible approaches proposed. Some of them look, reasonably enough, like the research equipment model above. Add a modest amount of money to grants earmarked for distribution to software, databases, or systems that the research group relies on. Maybe that would work! But it would almost certainly preferentially fund projects that are being actively worked on, taking feature requests and bug reports for software or new submissions for databases.
For mature, quiescent resources that “just work” and so fade into the background, the tools that don’t need development until they suddenly do, we need other solutions. Likely we need centres of expertise in research computing, populated by professionals as advocated by RSE societies around the world, with named maintainers even for research tools actively used but not actively developed.
People — maintainers, with the tools to do their job — are what drive software sustainability, not language choices or technologies. As a research community we need to find and retain funding to retain, develop, and empower those people to do their work. Otherwise we’re going to waste time and effort urgently re-learning and re-creating tools when individually unforeseeable but collectively predictable bursts in maintenance are needed.