A Comprehensive Study of Declarative Behavioural Modelling Languages
Bibliographic record
Abstract
Declarative behavioural modelling is a powerful modelling paradigm that enables users to model system functionality abstractly and formally using constraints. There are several different formal declarative languages and they have greatly varying constructs for representing a transition system, and they sometimes differ in rather subtle ways. We qualitatively compare seven formal declarative modelling languages B, Event-B, Alloy, Dash, TLA+, PlusCal, and Asmetal, with respect to three main categories: structuring transition systems (control modelling), data descriptions in transition systems (data modelling), and modularity aspects of modelling. We developed this comparison by completing a set of case studies across the data- vs. control-oriented spectrum of models in all of the above languages. We present recommendations based on our observations about the differentiating characteristics of each of these languages to help modellers answer the important question of which declarative modelling language may be most suitable for modelling their system.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".