2.3.2 COSYSMO Reuse Extension
Bibliographic record
Abstract
Abstract Reuse in systems engineering is a frequent, but poorly understood phenomenon. Nevertheless, it has a significant impact on estimating the appropriate amount of systems engineering effort with models like the Constructive Systems Engineering Cost Model. Practical experience showed that the initial version of COSYSMO, a model based on a “build from the scratch” philosophy, needed to be refined in order to incorporate reuse considerations that fit today's industry environment. The notion of reuse recognizes the effect of legacy system definition in engineering a system and introduces multiple reuse categories for classifying each of the four COSYSMO size drivers – requirements, interfaces, algorithms, and operational scenarios. It fundamentally modifies the counting rules for the COSYSMO size drivers and updates the definition of system size in COSYSMO. In this paper, we present (1) the definition of the COSYSMO reuse extension and the approach employed to define this extension; (2) the updated COSYSMO size driver definitions that are consistent with the reuse model; (3) the method applied to defining the reuse weights used in the modified parametric relationship; (4) a practical implementation example that instantiates the reuse model by an industry organization and the empirical data that provided practical validation of the extended COSYSMO model; and (5) recommendations for organizational implementation and deployment of this extension.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".