Panel on the role of graduate software and systems engineering bodies of knowledge in formulating graduate software engineering curricula
Bibliographic record
Abstract
The Software Engineering Body of Knowledge (SWEBOK), published in 2004, and now under revision, has influenced many software engineering graduate programs worldwide. In 2009, guidelines were published for graduate programs in software engineering (GSWE2009). GSWE2009, now sponsored by both the IEEE Computer Society and the Association for Computing Machinery, strongly rely on the SWEBOK but also recommends specific systems engineering knowledge for students to master. Today, an international team is creating a rigorous Systems Engineering Body of Knowledge (SEBoK) with the help of the IEEE Computer Society and the International Council on Systems Engineering and other professional societies. As it matures, the SEBoK should influence future versions of GSWE2009 and graduate program curricula worldwide. This panel will examine the influence of bodies of knowledge on both the creation of new graduate software engineering programs and the evolution of existing ones.
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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 | 0.051 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".