Earlier quoted context omitted.
The sole argument in this article seems to be that OO systems introduce more complexity than is justified which results in larger code bases than an FP-based system would, which increases maintenance costs. Then the article goes on to show some toy example solutions in FP style, without really touching on the challenges that don't show up until larger problems. I can write a great Account balance summer program in Py…
I'd really like someone on any side of this debate (and there are certainly more than two; for example, some people are advocates of "FP in the small, OO in the large") to write an article that does describe how their approach handles the challenges of designing and maintaining a large system. I think such articles are rare because they're much harder to write than something like this. In complex systems it becomes v…
The sucky part is that there is no guarantee that you will ever get there. A bad programmer or two and you've got a mess. Large FP systems crucially depend on high-quality coding. There is no place for everything to go, that's what the coders figure out!
Contrast that to an OO system, where things go where they naturally belong, but you really don't know what the algorithm is. Hell, you can spend days just wiring stuff up and putting stuff in place before the actual "real" code finds a home. But you always have a plan for where things go.
I don't think you can find a large, complex FP project because I think all the good complex FP projects are clusters of small executables.