Teaching students software engineering practices for micro-teams
Bibliographic record
Abstract
Standard methodologies, which have been developed for large software development teams, and Agile practices, developed for small teams, make up the software engineering practices taught in the Computer Science classroom. However, we have found that there is a significant prevalence of “micro” teams doing business-critical software development in the field. Thus, software development best practices for micro teams must be incorporated into the software curriculum. Towards this end, we created a multiple-case case study (comprising five micro team projects) showing how micro teams handle the software development process. Through each of these projects, we seek to showcase what practices from existing software development methodologies are undertaken by the developers of the projects, to achieve similar ends as developers in larger teams. Specifically, the case study highlights how existing software development methodologies need to be modified, adapted or extended for micro teams. The case study and micro team guidelines were presented to students in a software engineering class within the Computer Science department at a large R1 university. The teaching was assessed using a mix of surveys and structured interviews. Initial evaluations showed promise. Students were positively inclined to accept the lessons, and showed good recall of the concepts taught in tests.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".