Transitioning Towards SysML v2 as a Variability Modeling Language
Bibliographic record
Abstract
Abstract Systems engineers seek ways to reuse system models to accelerate the initial design development process on new complex projects. Despite its promise, one such solution -- the deployment of Model-based Product Line Engineering (MBPLE) -- presents challenges to practitioners. Not only is deployment costly, but an overwhelming number of variability modeling language options also lack interoperability. While research efforts work towards creating a universal variability modeling language and developing transformations between existing languages, this paper proposes incorporating variability modeling concepts into a system modeling language for a centralized management of product lines. In this work, we highlight SysML v2 as a promising language for variability modeling, arguing that it can orthogonally model systems and their variability. Further, we propose a novel metamodel that describes a SysML v2-based approach to modeling conceptual and asset variability spaces. We exemplify the deployment of this metamodel on a simple yet representative example found in a complex development environment. Since the SysML v2 specification is still in its early stages of standardization, this paper discusses how efforts in deploying MBPLE with existing approaches will translate to the new metamodel and includes considerations for transitioning from currently available commercial modeling tools to SysML v2. Ultimately, we hope the proposed metamodel will motivate SysML v2 tool vendors to support variability modeling by incorporating instantiation and constraint definition mechanisms and visualization capabilities in future tool releases.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".