Multi-formalism modelling and model transformation for the design of reactive systems
Bibliographic record
Abstract
based design. This paper presents a development process based on modelling, simulation, and code synthesis. The DCharts formalism, a Statecharts variant with extensions, is used to model a small application to demonstrate our approach: a traffic light. The development of this system highlights the use of various formalisms with appropriate supporting tools: AToM 3, A Tool for Multi-formalism and Meta-Modelling, is used as a multi-formalism visual modelling environment; SVM is the simulation engine used to experiment with prototype models; SCC is the code synthesizer that generates reusable source code in a variety of target languages. Transformation onto the Communicating Sequential Processes (CSP) formalism allows for model checking using the Failures Divergences Refinement Checker (FDR2) model checker. We demonstrate how using multiple formalisms as well as model transformations during the design process can drastically improve productivity, reliability and reusability. 1. MODELLING, ANALYSIS AND SIMU-LATION BASED DESIGN Compared to traditional software programming, modelling and simulation based (software) design has many advantages. By modelling the structure and behaviour of the system at an appropriate level of abstraction in the most appropriate formalism(s), accidental complexity will be minimized, and the designer can focus on essential issues instead of being bogged down with implementation details at early stages in the development process. 1.1. The process Our modelling and simulation based design process is illustrated in Figure 1. The system designer starts from a set of requirements, which constrain the design space. In the example given here, the requirements are not modelled explicitly
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".