This empirical research demonstrates the effectiveness of content analysis to map the research literature of the software engineering discipline. The results suggest that certain research themes in software engineering have remained constant, but with changing thrusts. Other themes have arisen, matured, and then faded as major research topics, while still others seem transient or immature. Co‐word analysis is the specific technique used. This methodology identifies associations among publication descriptors (indexing terms) from the ACM Computing Classification System and produces networks of descriptors that reveal these underlying patterns. This methodology is applicable to other domains with a supporting corpus of textual data. While this study utilizes index terms from a fixed taxonomy, that restriction is not inherent; the descriptors can be generated from the corpus. Hence, co‐word analysis and the supporting software tools employed here can provide unique insights into any discipline's evolution.

This empirical research demonstrates the effectiveness of content analysis to map the research literature of the software engineering discipline. The results suggest that certain research themes in software engineering have remained constant, but with changing thrusts. Other themes have arisen, matured, and then faded as major research topics, while still others seem transient or immature. Co-word analysis is the specific technique used. This methodology identifies associations among publication descriptors (indexing terms) from the ACM Computing Classification System and produces networks of descriptors that reveal these underlying patterns. This methodology is applicable to other domains with a supporting corpus of textual data. While this study utilizes index terms from a fixed taxonomy, that restriction is not inherent; the descriptors can be generated from the corpus. Hence, co-word analysis and the supporting software tools employed here can provide unique insights into any discipline's evolution.

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