FORMATION OF SOFTWARE DESIGN SKILLS AMONG SOFTWARE ENGINEERING STUDENTS
Bibliographic record
Abstract
The paper is devoted to the one of the competence components of a mobile-oriented environment for software engineering (SE) students. It is shown that the introduction of the higher education standard for SE bachelors has created a number of problems to ensure the quality of training, primarily related to low level of specification both for competencies and learning outcomes. The way to solve these problems is to design a detailed system of professional competencies for SE bachelors.The paper considers approaches to the formation of the important special professional competency of future software engineers – K14 (ability to participate in software design, including modeling (formal description) of its structure, behavior and functioning processes). Based on a historical and genetic review of the software design training among SE students in the UK, USA, Canada, Australia, New Zealand and Singapore, recommendations for choosing of software design teaching techniques, selection of learning content, modeling and design tools, assessment of the level of formation of the relevant competence are developed. The example of the industrial-like software design training (studio training) is considered. The problems of transition from architectural to detailed design and project implementation are shown.Prospects for further development of this study are to substantiate the third (after requirements engineering and design engineering) engineering component of SE – software constructing.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".