Implementación del Estándar ISO/IEC 29110 en Centros de Desarrollo de Software de Universidades Mexicanas: Experiencia del Estado de Zacatecas
Bibliographic record
Abstract
Resumen: Hoy en día las empresas muy pequeñas de desarrollo de software tienen una gran presencia en la industria del software en cualquier país, donde alrededor del 94% está formado por este tipo de empresas.Esta situación resalta la creciente necesidad de mejorar sus procesos de desarrollo de software que les permitan desarrollar productos y servicios de calidad.Para lograrlo se deben cumplir dos requisitos: (1) integrar una cultura de procesos y mejora continua en las organizaciones, y (2) dotar de personal altamente cualificado que tenga el conocimiento y habilidades para trabajar exitosamente con modelos y/o estándares utilizados en las organizaciones.Este artículo presenta la experiencia de la implementación del estándar ISO/IEC 29110 para reforzar el proceso de desarrollo de software de cuatro centros de desarrollo de software de universidades mexicanas como una solución para abordar con los dos requisitos previamente mencionados.
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 | 0.004 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".