There\'s a widespread belief among programmers that the more dynamic and loosely

Question

There's a widespread belief among programmers that the more dynamic and loosely typed the language, the more productive the programmer will be in it. Guido van Rossum wrote about programming productivity using python in 1998 and searching around the web I still see people referencing this exact claim:

Syntactically, Python code looks like executable pseudo code. Program development using Python is 5-10 times faster than using C/C++, and 3-5 times faster than using Java. In many cases, a prototype of an application can be written in Python without writing any C/C++/Java code. Often, the prototype is sufficiently functional and performs well enough to be delivered as the final product, saving considerable development time. Other times, the prototype can be translated in part or in whole to C++ or Java -- Python's object-oriented nature makes the translation a straightforward process.

Has this issue been properly scientifically evaluated? If not for python then perhaps for sibling scripting languages like ruby, perl or php?

I'm not looking for rationalizations, analogies, or explanations why it could potentially be hard to answer, unless it's the opinion of researchers or experts that has taken the time to look into the issue.

I initially asked this question over at skeptics.SE, and someone suggested I should ask it here too.

Explanation / Answer

Ousterhout's article1 about scripting languages suggests that the higher level the programming takes place, the more productive the programmer is. If we take that, as Boehm says2, the number of lines a programmer can write in a given time is constant and not dependent on the language or its type (low level, system programming, scripting), one can easily believe the claim. The resulting instructions-per-source-code-line -ratio can be an order of magnitude (or several) better with scripting languages than with system programming languages.

As scripting languages heavily rely on ready-made utilities for common tasks (e.g. data structures, string manipulation), their main use usually is to enhance productivity with the cost of slower running speed by providing a syntax that's easy to learn and efficient to upkeep programs with. One doesn't resort to a scripting language when top execution speed is needed.

[1]: J. K. Ousterhout, Scripting: Higher Level Programming for the 21 Century, Computer (IEEE), 1998
[2]: B. Boehm, Software Engineering Economics, Prentice Hall, 1981

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