Why bad scientific code beats code following "best practices"
I am a scientist, and I have seen a lot of terrible code. Most scientists have no formal training in computer science or coding. Many advisors don't place much value in having their grad students take such classes, though even a short language-specific introduction class would vastly improve their students' productivity.<p>I recently undertook a complete rewrite of our group's analysis software that was written by our previous postdoc. It was ~30k lines of code in 2 files (one header, one source file), with pretty much every bad coding practice you can image. It was so complicated that that postdoc was essentially the only one who could make changes and add features.<p>The rewritten framework is only ~6k lines of code to replicate the exact same functionality. It's easy enough to use that just by following some examples, the grad students have been able to do implement studies in a couple days that took weeks in the old framework. The holy grail is for it to be easy enough for the faculty to use, but that will probably take a dedicated tutorial.<p>My point is that following "best practices" may be overkill, but taking a thoughtful approach to the design of the software can vastly improve your productivity in the long run. Posts like the OP help scientists who write bad code defend poor practices. Any scientist worth his salt should support following good practices because it will always lead to better science.