The use of bibliometric and knowledge elicitation techniques to map a knowledge domain: Software Engineering in the 1990s

Katherine W. McCain, June M. Verner, Gregory W. Hislop, William Evanco and Vera Cole
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Katherine W. McCain: College of Information Science & Technology, Drexel University
June M. Verner: National ICT Australia
Gregory W. Hislop: College of Information Science & Technology, Drexel University
William Evanco: College of Information Science & Technology, Drexel University
Vera Cole: Interlace Corporation

Scientometrics, 2005, vol. 65, issue 1, No 6, 144 pages

Abstract: Summary Parallel mappings of the intellectual and cognitive structure of Software Engineering (SE) were conducted using Author Cocitation Analysis (ACA), PFNet Analysis, and card sorting, a Knowledge Elicitation (KE) method. Cocitation counts for 60 prominent SE authors over the period 1990 - 1997 were gathered from SCISEARCH. Forty-six software engineers provided similar data by sorting authors’ names into labeled piles. At the 8 cluster level, ACA and KE identified similar author clusters representing key areas of SE research and application, though the KE labels suggested some differences between the way that the authors’ works were used and how they were perceived by respondents. In both maps, the clusters were arranged along a horizontal axis moving from “micro” to “macro” level R&D activities (correlation of X axis coordinates = 0.73). The vertical axis of the two maps differed (correlation of Y axis coordinates = -0.08). The Y axis of the ACA map pointed to a continuum of high to low formal content in published work, whereas the Y axis of the KE map was anchored at the bottom by “generalist” authors and at the top by authors identified with a single, highly specific and consistent specialty. The PFNet of the raw ACA counts identified Boehm, Basili, and Booch as central figures in subregions of the network with Boehm being connected directly or through a single intervening author with just over 50% of the author set. The ACA and KE combination provides a richer picture of the knowledge domain and provide useful cross-validation.

Date: 2005
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DOI: 10.1007/s11192-005-0262-7

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