ISO/IEC 29110 Deployment Packages and Case Study for Systems Engineering: The “Not‐So‐Secret” Ingredients That Power the Standard
Bibliographic record
Abstract
Abstract Very small entities (VSEs) play an increasingly important role in the global economy. The products they develop are often integrated into products made by larger enterprises. Clients, furthermore, demand of the VSEs that they assume a much broader role, spanning the entire development life‐cycle of the product instead of being limited to a “build‐to‐print” approach. The ISO/IEC 29110 systems engineering management and engineering guides were developed mainly from ISO/IEC/IEEE 15288 to address this new reality, to exploit the lean and efficient nature of VSEs and to adapt to their typical budget and resource constraints. By design, the management and engineering guide is supported by Deployment Packages (DP), the development of which was taken on by the INCOSE VSE Working Group. A DP is a set of artefacts designed to facilitate the implementation of the management and engineering guides of ISO/IEC 29110 by VSEs. In tune with the need for low cost and flexibility, Open Source software tools are emerging to support VSEs and provide a bridge with “Big League” development life‐cycle toolsets. Finally, to make the deployment of ISO/IEC 29110 possible in VSEs, training packages, supported by relevant pilot projects help VSE personnel learn how to apply all of the above. This paper describes the Systems Engineering DP for Requirements Engineering (RE DP) and shows how it can be applied using the Autonomous Rover Case Study developed under the Eclipse Foundation Polarsys project.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".