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The case for collaborative programming

Open access
Nosek, John T.·1998·Communications of the ACM

Abstract

This article presents a technical opinion in the case of collaborative programming for a large, complex system, in which two programmers are working jointly on the same algorithm and code. A field experiment was conducted using experienced programmers who worked on a challenging problem important to their organization, in their own environments, and with their own equipment. Findings revealed that all the teams outperformed the individual programmers, enjoyed the problem-solving process more, and had greater confidence in their solutions. Several aspects of this experiment make the results significant. First, there may be a tendency to dismiss results using small groups. Questions may arise at the value of these results if the collaborators do not perform twice as well as individuals, at least in the amount of time spent. The article concludes that the crunch-time information systems development will demand innovative ways to produce high-quality systems in a short time, with companies increasingly introducing new products. This rare experimental research is phenomenon considering the luxury of using collaborative programming while many companies are experiencing shortages of experienced programmer/analysts.

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Metadata

Authors
Nosek, J. T.
Venue
Communications of the ACM
Published
1998-03
Volume
41
Issue
3
Pages
105-108
ISSN
  • 0001-0782 (ISSN-L)
  • 1557-7317
OpenAlex
W2049329210
Cited by
375
References
0
FWCI
13.79
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: The case for collaborative programming. https://app.cochat.ai/papers/10.1145/272287.272333
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Nosek, J. T. (1998). The case for collaborative programming. Communications of the ACM. https://doi.org/10.1145/272287.272333

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.