Analysis of 13 implementations of the software engineering management and engineering basic profile guide of ISO/IEC 29110 in very small entities using different life cycles
Bibliographic record
Abstract
Abstract We are living in an age of growing demand for software products. This growing demand creates opportunities for very small entities (VSEs) to not only survive but also flourish. In this context, VSEs need to produce high‐quality products to meet market needs. However, in their quest to produce high‐quality software, VSEs need to overcome the challenge of implementing international standards, which they find difficult to do because of lack of knowledge and practical experience. This paper provides an analysis performed to 13 teams of VSEs, using different life cycles, which achieved the implementation of the ISO/IEC 29110, to analyze the effort each team invests to implement the best practices provided by the standard. Besides, the paper provides an analysis of the difficulties, and the benefits of using the six‐step method are included. This analysis is of interest because software engineering knowledge developed by researchers should be transferred to the industry to reduce the gap between them. The results highlight, on the one hand, the practices representing more effort for teams by life cycle. On the other hand, the results highlight the six‐step method that allowed the 13 teams to achieve a high level of coverage of the ISO/IEC 29110 international standard.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".