I've spent a bit of time on the inside at NASA, specifically working on earth observing systems. There is a huge difference between the code quality of things that go into control systems for spacecraft (even then, meters vs. feet, really?) and the sort of analysis/theoretical code the article talks about. Spacecraft code gets real programmers and disciplined practices, while scientific code is generally spaghetti IDL/Matlab/Fortran.

There is a huge problem with even getting existing code to run on different machines. My team's work was primarily dealing with taking lots of project code (always emailed around, with versions in the file name) and rewriting it to produce data products that other people could even just view. Generally we'd just pull things like color coding out of the existing code and then write our processors from some combination of specifications and experimentation.

I'd agree that "unit tests" and trendy best practices are probably not the full answer, but the article is correct in emphasizing documentation, modularity, and source control. Source control alone would protect against bugs produced by simply running the wrong version of code.