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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".