Implementation and Certification of ISO/IEC 29110 in an IT Startup in Peru
Bibliographic record
Abstract
This article presents the implementation of ISO/IEC 29110 in a four-person IT startup company in Peru. After completing the implementation of the ISO/IEC 29110 project management and software implementation processes using an agile approach, the next step was to execute these processes in a project with an actual customer: software that facilitates communication between clients and legal consultants at the second-largest insurance companies in Peru. Managing the project and developing the software took about 900 hours. Using ISO/ IEC 29110 software engineering practices enabled the startup to plan and execute the project while expending only 18 percent of the total project effort on rework (i.e., wasted effort). In this article, the authors also describe the steps and the effort required by the VSE to be granted an ISO/IEC 29110 certificate of conformity. The startup became the first Peruvian VSE to obtain an ISO/IEC 29110 certification. The ISO/IEC 29110 certification facilitated access to new clients and larger projects.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".