MBSE delivers significant return on investment in evolutionary development of complex SoS
Bibliographic record
Abstract
Abstract The Submarine Warfare Federated Tactical Systems (SWFTS) is a rapidly evolving combat system of systems (SoS) product family. Managing the annual baseline updates requires processing thousands of baseline change requests, then coordinating and verifying their implementation. The complexity of this effort, which involves well over ten million source‐lines‐of‐code (SLOC) as well as Commercial‐Off‐the‐Shelf (COTS) and military‐unique hardware, is compounded by being deployed in ten variants. After a feasibility study in 2010 the SWFTS systems engineering and integration program started a transition from traditional requirements database and document‐centric systems engineering (DCSE) to a model‐based systems engineering (MBSE) process. At that time there was little solid evidence in the literature for a positive Return on Investment (ROI) for moving from DCSE to MBSE. Applying MBSE to this program has resulted in measurable monetary and operational benefits. We 1) summarize the DCSE to MBSE transition, 2) describe the accomplishments and observations to date, 3) define the metrics collected, and 4) quantify the achieved ROI. Background on the systems engineering and integration (SE&I) process and an apples‐to‐apples comparison of SE quality and efficiency are provided. The raw SE&I efficiencies of the DCSE and MBSE approaches are documented, along with conclusions showing that the MBSE approach delivers a positive ROI through higher quality SE products at significantly less cost‐per‐change, enables managing more baselines and SoS complexity using constant resources, and reduces the cost of the downstream integration effort.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".