A BIBLIOMETRIC/GEOGRAPHIC ASSESSMENT OF 40 YEARS OF SOFTWARE ENGINEERING RESEARCH (1969–2009)
Bibliographic record
Abstract
Bibliometric rankings are quite common in the field of software engineering. For example, there are a series of ranking repeated every year which identify the top researchers and institutions at the international level in the field. There are also other studies to determine the most cited articles in software engineering journals, the most popular research topics in this area, or identify the top researchers and institutions in regional levels. However, there exists no existing bibliometric quantitative analysis of publications in the area of software engineering (SE), including relative and absolute growth in the number of all SE publications as well as an analysis among countries. This is the main goal and motivation of this article. Besides, this study intends to provide an overall quantitative trend of the software engineering papers, and compare that trend to research output in other areas of science. The bibliometric study reported in this paper is motivated by the fact that understanding the amount of geographical research contributions to the field of software engineering can help identify different countries’ level of commitment to support research activities in this area over years. We analyze how the contribution levels of top-ranked countries have changed over the years and how SE compares to other disciplines of engineering and science. Among the most interesting findings of this study are: (1) Over 40 years, in total about 60% of the SE literature has been contributed by only 7% of all countries, (2) the SE research output of different countries does not necessarily correlate with their GDPs, (3) the share of contributions to the SE discipline by the American researchers has declined from 71.43% (in 1980) to 14.90% (in 2008), and (4) China is the country with the biggest share growth in the number of publications (from 0.82% of the entire SE publications in 1991 to 13.82% in 2009).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.149 | 0.172 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".