Formal representation of SysML/KAOS domain model
Bibliographic record
Abstract
Nowadays, the usefulness of a formal language for ensuring the consistency of requirements is well established. The work presented here is part of the definition of a formally-grounded, model-based requirements engineering method for critical and complex systems. Requirements are captured through the SysML/KAOS method and the targeted formal specification is written using the Event-B method. Firstly, an Event-B skeleton is produced from the goal hierarchy provided by the SysML/KAOS goal model. This skeleton is then completed in a second step by the Event-B specification obtained from system application domain properties that gives rise to the system structure. Considering that the domain is represented using ontologies through the SysML/KAOS Domain Model method, is it possible to automatically produce the structural part of system Event-B models ? This paper proposes a set of generic rules that translate SysML/KAOS domain ontologies into an Event-B specification. They are illustrated through a case study dealing with a landing gear system. Our proposition makes it possible to automatically obtain, from a representation of the system application domain in the form of ontologies, the structural part of the Event-B specification which will be used to formally validate the consistency of system requirements.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".