> My friend clarifies: "It's not the efficiency of doxygen that's the > question. The problem is that you can add fields to objects as you go in > Python so you need to do a deep analysis of the code to determine the class > structure which you don't have to do with C++ (or Java)."
That's true itds one of the "benefits" of a dynamic language, but it does make the code harder to parse. > He mentioned numbers like maybe ~20+x slower on lines-of-code > for Python vs C++. That sounds high, I would have expected no more than 5-10 times longer. But of course against that we have the advantage that there will be far fewer lines to parse in a Python project, typically only a third or a quarter of the number of lines - sometimes less than that. > A second friend of mine who is an XML/Java enthusiast echoed similar > comments about scalability in large builds with weakly-typed dynamic > languages such as Python. The concept of a "large build" doesn't really exist in an interpreted language like Python. OTOH I probably wouldn't usePython for a very large project - say over a million lines of code in C++ - for a variety of other reasons. eg. Python could do it but the coordination of multi team projects becomes harder without tools like static type checking. Alan G _______________________________________________ Tutor maillist - Tutor@python.org http://mail.python.org/mailman/listinfo/tutor
