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Experiments in Computing: A Survey

Authors: Matti Tedre; Nella Moisseinen;

Experiments in Computing: A Survey

Abstract

Experiments play a central role in science. The role of experiments in computing is, however, unclear. Questions about the relevance of experiments in computing attracted little attention until the 1980s. As the discipline then saw a push towards experimental computer science, a variety of technically, theoretically, and empirically oriented views on experiments emerged. As a consequence of those debates, today's computing fields use experiments and experiment terminology in a variety of ways. This paper analyzes experimentation debates in computing. It presents five ways in which debaters have conceptualized experiments in computing: feasibility experiment, trial experiment, field experiment, comparison experiment, and controlled experiment. This paper has three aims: to clarify experiment terminology in computing; to contribute to disciplinary self-understanding of computing; and, due to computing’s centrality in other fields, to promote understanding of experiments in modern science in general.

Country
Finland
Keywords

Technology, Computer and information sciences, T, Science, Data Collection, Research, SCIENTIST, MODELS, Q, R, SCIENCE, Review Article, Computing Methodologies, COMPUTATIONAL-COMPLEXITY, VALIDATION, MATHEMATICS, Research Design, PROGRAM, DISCIPLINE, Medicine, TECHNOLOGY, MYTHS

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    20
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Top 10%
Green
gold