Citation Analysis of Computer Systems Papers
Eitan Frachtenberg

TL;DR
This study analyzes citation patterns in computer systems research, revealing rapid citation accumulation, the influence of preprints and self-citations, and database consistency in citation metrics.
Contribution
It provides a comprehensive analysis of citation dynamics in computer systems, comparing databases and examining self-citation characteristics, which was previously underexplored.
Findings
Most papers are cited within a year of publication.
Highly cited papers tend to have more external citations over time.
Database choice has little impact on relative citation comparisons.
Abstract
Citation analysis is used extensively in the bibliometrics literature to assess the impact of individual works, researchers, institutions, and even entire fields of study. In this paper, we analyze citations in one large and influential field within computer science, namely computer systems. Using citation data from a cross-sectional sample of 2,088 papers in 50 systems conferences from 2017, we examine four research questions: overall distribution of systems citations; their evolution over time; the differences between databases (Google Scholar and Scopus) for systems papers, and; the characteristics of self-citations in the field. We find that only 1.5% of papers remain uncited after five years, while 12.8% accrued at least 100 citations, both statistics comparing favorably to many other scientific fields. The most cited subfields and conference areas within systems were security,…
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Taxonomy
TopicsSoftware Engineering Research · Software System Performance and Reliability · Online Learning and Analytics
