Software engineering body of knowledge (SWEBOK) (panel session)
Bibliographic record
Abstract
The goals of the SWEBOK project has been to develop a topical guide to the body of knowledge (BoK) supporting the discipline of software engineering. The project, sponsored by IEEE Computer Society, is over three years old and is nearing completion of its third and final stage. However, there has been some disagreement as to whether there is currently a common core software engineering body of knowledge at its current stage of evolution, and if so, what is size and contents of that BoK. This panel will present the current status of the SWEBOK and discuss its strengths and weakness, as well as address the more general question of the possible existence and nature of a software engineering body of knowledge. Issues related to internationalization, certification, and accreditation will be examined. The need for various computing societies to be invited to and contribute to the improvement of the SWEBOK project will also be highlighted.The panel discussion will have two parts: The first part will be an informative session. A short history will be presented and issues related to the curriculum, accreditation, and the maturity of the field to warrant a defined BoK will be discussed. In the second part, the panel members will discuss and debate the planned experimentation of the guide, its shortcomings, and how various computing societies may and should cooperate to improve the guide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.039 |
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".