<i>checsdm</i>: A Method for Ensuring Consistency in Heterogeneous Safety-Critical System Design
Bibliographic record
Abstract
Safety-critical systems are highly heterogeneous, combining different characteristics. Effectively designing such systems requires a complex modelling approach that deals with diverse components (e.g., mechanical, electronic, software)—each having its own underlying domain theories and vocabularies—as well as with various aspects of the same component (e.g., function, structure, behaviour). Furthermore, the regulated nature of such systems prescribes the objectives for their design verification and validation. This paper proposeschecsdm, a systematic approach, based on Model-Driven Engineering (MDE), for assisting engineering teams in ensuring consistency of heterogeneous design of safety-critical systems. The approach is developed as a genericmethodologyand atool framework, that can be applied to various design scenarios involving different modelling languages and different design guidelines. The methodology comprises an iterative three-phased process. The first phase,elicitation, aims at specifying requirements of the heterogeneous design scenario. Using the proposed tool framework, the second phase,codification, consists in building a particular tool set that supports the heterogeneous design scenario and helps engineers in flagging consistency errors for review and eventual correction. The third phase,operation, applies the tool set to actual system designs. Empirical evaluation of the work is presented through two executions of thechecsdmapproach for the specific cases of a design scenario involving a mix of UML, Simulink and Stateflow, and a design scenario involving a mix of AADL, Simulink and Stateflow. Theoperationphase of the first case was performed over three avionics systems and the identified inconsistencies in the design models of these systems were compared to the results of a fully manual verification carried out by professional engineers. The evaluation also includes an assessment workshop with industrial practitioners to examine their perceptions about the approach. The empirical validation indicates the feasibility and “cost-effectiveness” of the approach. Inconsistencies were identified in the three avionics systems with a greater recall rate over the manual verification. The assessment workshop shows the practitioners found the approach easy to understand and gave an overall likelihood of adoption within the context of their work.
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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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".