Implementing ISO/IEC 29110 to reinforce four very small entities of Mexico under an agile approach
Bibliographic record
Abstract
Very small entities (VSEs) of software development have had a significant demand and economic impact in recent years, because most of them are the software producers for medium and big companies in order to satisfy the growing demand of software. In this context, it is important to ensure that they produce quality software to successfully meet the market needs. This task relies on having the knowledge and the experience to implement proven practices, which are contained in quality models and standards, to be able to develop quality software, while increasing their productivity and keeping or reducing their costs. A description of the implementation of ISO/IEC 29110 in Mexico, specifically at Zacatecas State is presented. This implementation was done as a strategy to increase the competitiveness of them. The study includes both, the strategy established to deploy the knowledge and the method followed to implement the ISO/IEC 29110 in four VSEs that uses scrum methodology as agile approach. The results show that the implementation of ISO/IEC 29110 was easy and with a high acceptance due to the benefits detected in the reinforcement of the VSEs’ processes without changing the way they work and solving their problems.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".