Multi-formalism modelling and model transformation for the design of reactive systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it