Composable semantics for model-based notations
Bibliographic record
Abstract
We propose a unifying framework for model-based specification notations. Our framework captures the execution semantics that are common among model-based notations, and leaves the distinct elements to be defined by a set of parameters. The basic components of a specification are non-concurrent state-transition machines which are combined by composition operators to form more complex, concurrent specifications. We define the step-semantics of these basic components in terms of an operational semantics template whose parameters specialize both the enabling of transitions and transitions' effects. We also provide the operational semantics of seven composition operators, defining each as the concurrent execution of components, with changes to their shared variables and events to reflect inter-component communication and synchronization; the definitions of these operators use the template parameters to preserve in composition notation-specific behaviour. By separating a notation's step-semantics from its composition and concurrency operators, we simplify the definitions of both. Our framework is sufficient to capture the semantics of basic transition systems, CSP, CCS, basic LOTOS, ESTELLE, a subset of SDL88, and a variety of statecharts notations. We believe that a description of a notation's semantics in our framework can be used as input to a tool that automatically generates formal analysis tools.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".