Automatic code generation within student's software engineering projects
Bibliographic record
Abstract
In this paper, we describe the integration of research and new teaching strategies into computer science and engineering departments at universities and colleges related to the automatic code generation, automatic development tools and integrated environments within student software (software) engineering and research projects. A significant amount of current software engineering research is conducted within the context of computer science, computing and engineering departments or colleges. However, every computer department has its own experiences, successes or pitfalls in Software Engineering and software development teaching and student research integration, which would be useful to share and discuss with the education community. We will discuss our experiences and results from seven years of teaching Software EngineeringCoSc 470/471 and Projects in Computer Science CoSc 224 (PCS) in Computer Information Systems (CIS) diploma and Bachelor of Computer Information Systems (BCIS) degree programs at Okanagan College (OC), Université Paris-Est Créteil (UPEC), France and almost 2 years of teaching at University of British Columbia Okanagan (UBC O). We suggest possible ways of integrating Software Engineering research and teaching strategies by using many different industrial software Engineering tools, development workbenches, frameworks and environments to automatize code generation and speed up student project development within student capstone projects into computer science departments at universities and colleges.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".