Bad scientific code beats code following "best practices" (2014)

You can't solve the first 3 issues without having people who care about software quality. People not caring about the quality of the software is what caused those initial 3 problems in the first place.

* "Big ball of mud" design [0]: No thought given to how the software should be architected or what the entities that comprise the design space of the problem are and how they fit together. The symptoms of this lack of thinking are obvious: multi-thousand-line swiss-army-knife functions, blocks of code repeated in dozens of places with minor variations, and a total lack of composability of any components. This kind of software design (or lack of design, really) ends up causing a serious hit to productivity because it's often useless outside of the narrow problem it was written to solve and because it's exceedingly hard to maintain or add new features to.

* Lack of tests: some of this is that the scientist-turned-programmer doesn't want to "waste time" writing tests, but more often it's that they don't know _how_ to write good tests. Or they have designed the code in such a way (see above) that it's really hard to test. In any case--unsurprisingly--their code tends to be buggy.

* Lack of familiarity with common data structures and algorithms: this often results in overly-complicated brute-force solutions to problems being used when they needn't have and in sub-par performance.

This quote from the author stood out to me:

> I claim to have repented, mostly. I try rather hard to keep things boringly simple.

...because it's really odd to me. Writing code that is as simple as it can be is precisely what good programmers do! But in order to get to the simplest possible solution to a non-trivial problem you need to think hard about the design of the code and ensure that the abstractions you implement are the right ones for the problem space. Following the "unix philosophy" of building small, simple components that each do one thing well but are highly composable is undoubtedly the more "boringly simple" approach in terms of the final result, but it's a harder to do (in the sense that it may take more though and more experience) than diving into the problem without thinking and cranking out a big ball of mud. Similarly reaching for the correct data structure or algorithm often results in a massively simpler solution to your problem, but you have to know about it or be willing to research the problem a bit to find it.

The author did at least try to support his thesis with examples of "bad things software engineers do", but a lot of them seem like things that--in almost every organization I've worked at in the last ten years--would definitely be looked down on/would not pass code review. Or are things ("A forest of near-identical names along the lines of DriverController, ControllerManager, DriverManager, ManagerController, controlDriver") that are narrowly tailored to a specific language at a specific window in time.

> they care too much about the quality of their work and not enough about getting shit done.

I think the appearance of "I'm just getting shit done" is often a superficial one, because it doesn't factor in the real costs: other scientists and engineers can't use their solutions because they're not designed in a way that makes them work in any other setting than the narrow one they were solving for. Or other scientists and engineers have trouble using the person's solutions because they are hard to understand and badly-documented. Or other scientists and engineers spend time going back and fixing the person's solutions later because they are buggy or slow. The mindset of "let's just get shit done and crank this out as fast as we can" might be fine in a research setting where, once you've solved the problem, you can abandon it and move on to the next thing. But in a commercial setting (i.e. at a company that builds and maintains software critical for the organization to function) this mindset often starts to impose greater and greater maintenance costs over time.

[0] https://en.wikipedia.org/wiki/Anti-pattern#Big_ball_of_mud

One thing I've noticed is that programming practices have evolved, not so much to make them better than before, though that's conceivable. But because practices have to keep up with rising complexity of the code itself, and also of the operating environment and the social environment (e.g., work teams, open source projects, etc).

Scientific programs tend to be easily 20 years behind software development in terms of complexity, and I think we can benefit from using older techniques that were simpler and easier to learn. I learned "structured programming" via Pascal, and to this day if I hew to the same practices that I learned in my Pascal textbook, my program will probably do what it needs to do and be tolerably maintainable.

Perhaps those practices have to come from the mouths of scientists. The software engineers have moved on, and are only interested in the latest and greatest toys. I don't blame them -- they have to own their careers and follow their interests just like we do.

I mentor younger scientists who come out of fields such as chemistry, and are beginners at coding. So I literally get to explain such basic things as putting code inside subroutines, and avoiding global variables. I haven't had to tell anybody about GOTO's yet.

About reproducibility: My parents were both scientists, though my mom spent a few years in mid-career teaching programming at a community college. I learned the scientific method sitting on my mommy's knee. "Reproducibility" was certainly a guiding principle, but it was also expected that reproducing a result would require some effort -- perhaps fabricating your own equipment from available materials, and gaining skill on a technique. You might get it wrong many times before finally getting it right.

What we expect now is "pushbutton" reproducibility, meaning that a project replicates itself from start to finish at the push of a button. This is a much higher standard than any scientist is trained to expect, even if software engineering requires it. A software project has to be at least 99% that way, or it would be unworkable, due to the high degree of complexity. The tradeoff is that it also requires complexity to make things that way.

I expect my results to be reproducible, but not pushbutton-reproducible. To overcome this issue, I'd rather spend my time documenting my code and its theory of operation, than making it bulletproof. Nothing that I write goes directly into production, and I expect the theory of operation to be more valuable to a project than my code. Often, the code just automates an experiment to test the theory, so it's a middleman rather than a product.