2002Unpublished venueRequires access

On measuring programmer team productivity

L. F. Johnson

Open publisher page 5 citations

Abstract

It is difficult to study industrial programmer productivity because of the extreme variance seen among individual programmers and the difficulty of performing controlled experiments. As an alternative to studying individual programmers, we examine the group productivity of programmer teams. We postulate that there is such a thing as average programmer productivity, in a given context. By studying programmer teams, we can eventually obtain measures of the expected performance of an average programmer in a defined context. Differences in project productivity can then be attributed to process characteristics. Existing project data is examined to see how data could be collected to support the idea of a standard programmer.

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What this paper is about

It is difficult to study industrial programmer productivity because of the extreme variance seen among individual programmers and the difficulty of performing controlled experiments. As an alternative to studying individual programmers, we examine the group productivity of programmer teams. We postulate that there is such a thing as average programmer productivity, in a given context. By studying programmer teams, we can eventually obtain measures of the expected performance of an average programmer in a defined context. Differences in project productivity can then be attributed to process characteristics. Existing project data is examined to see how data could be collected to support the idea of a standard programmer.

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OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

It is difficult to study industrial programmer productivity because of the extreme variance seen among individual programmers and the difficulty of performing controlled experiments. As an alternative to studying individual programmers, we examine the group productivity of programmer teams. We postulate that there is such a thing as average programmer productivity, in a given context. By studying programmer teams, we can eventually obtain measures of the expected performance of an average programmer in a defined context. Differences in project productivity can then be attributed to process characteristics. Existing project data is examined to see how data could be collected to support the idea of a standard programmer.

Key concepts: Programmer, Productivity, Computer science, Context (archaeology), Process (computing), Variance (accounting), Software engineering, Programming language

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