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A study shows that lines_written/time is language-independent and application-independent for most programmers. If this were true it would imply that the most terse a language is, the more productive a programmer can be on it.

Where can this study be found?

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No, it would imply that programmers can yank out more code per time unit the terser the language used is. There are several important differences between "code produced" and "productivity" -- such as correctness, debug-ability, and maintainability. – delnan Aug 18 '12 at 21:29
@delnan "No, it would imply that programmers can yank out more code per time unit the terser the language used is." How so? Wouldn't it imply (or rather: state directly) that the amount of code stays the same? – sepp2k Aug 18 '12 at 22:20
@sepp2k Ambiguous, I admit. 100 lines of Python are "more code" than 100 lines of Java in the same sense as 100 Euros are (at least as of now) "more money" than 100 USD. That is, same number but different value per unit -- one buys you more stuff than the other, or in this case, does more stuff. – delnan Aug 18 '12 at 22:37
@delnan: So on one hand there is terseness in terms of how much you have to type (more or less concise syntax), on the other hand in terms of how abstract the language is: the more abstract it is the more stuff you can do with less code. – Giorgio Aug 19 '12 at 16:06
The point is, if it is true that programmers can write the same amount of lines in some period regardless of the language (which I question), that would obviously imply that the most code dense language would allow for writting of programs faster. – Dokkat Aug 20 '12 at 0:16
up vote 8 down vote accepted

Well top result of web search for "lines written time is programming language independent" led me to an article that attributes this to "The Mythical Man-Month" by Brooks:

Brooks is generally credited with the assertion that annual lines-of-code programmer productivity is constant, independent of programming language. In making this assertion, Brooks cites multiple authors including [7] and [8]. Brooks states, “Productivity seems constant in terms of elementary statements, a conclusion that is reasonable in terms of the thought a statement requires and the errors it may include.” [1] (p. 94)...

[1] F. P. Brooks. The Mythical Man-Month: Essays on Software Engineering.
Addison Wesley, Boston, MA, 1995.
[7] W. M. Taliaffero. Modularity. the key to system growth potential.
IEEE Software, 1(3):245–257, July 1971.
[8] R. W. Wolverton. The cost of developing largescale software.
IEEE Transactions on Computers, C-23(6):615–636, June 1974.

Article quoted above is "Do Programming Languages Affect Productivity? A Case Study Using Data from Open Source Projects" by D. Delorey, C. Knutson, S. Chun.

For the sake of completeness note that article authors are skeptical about mentioned assumption:

This statement, as well as the works it cites... appears to be based primarily on anecdotal evidence.

Quite the opposite, they claim:

We examine data collected from the CVS repositories of 9,999 open source projects hosted on to test this assumption for 10 of the most popular programming languages in use in the open source community. We find that for 24 of the 45 pairwise comparisons, the programming language is a significant factor in determining the rate at which source code is written, even after accounting for variations between programmers and projects

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This is exactly what I asked for; and the assertion is probably wrong, as I expected. Intelligent method they used! – Dokkat Aug 20 '12 at 0:18

Here are the references I know of:

“An Empirical Comparison of Seven Programming Languages” by Lutz Prechlt, University of Karlsruhe.

1968, referenced in Mythical Man-Month: PL/I lines/year comparable to Assembler words/year

1971, referenced in Mythical Man-Month: Assembler, Fortran, Cobol: roughly equal

1981: “Amount of effort per source statement was highly independent of language level” (Barry W. Boehm’s “Software Engineering Economics” p. 477)

1970: High level languages 3 times as productive as Assembler

Walston-Felix, 1977 "A method of programming measurement and estimation", IBM Syst. J., 16, 1, 1977, pp. 54-73.

Nelson, 1978 "Software data collection and analysis", Rome Air Development Center, Rome, NY, September, 1978

References found in:

Chapter 8 of “The Mythical Man-Month” by Fred Brooks

Barry W. Boehm’s “Software Engineering Economics” p. 477

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