CoreASM: an extensible modeling framework & tool environment for high-level design and analysis of distributed systems
Bibliographic record
Abstract
Model-based systems engineering naturally requires abstract executable specifications to facilitate simulation and testing in early stages of the system design process. Abstraction and formalization provide effective instruments for establishing critical system requirements by precisely modeling the system prior to construction so that one can analyze and reason about specification and design choices and better understand their implications. There are many approaches to formal modeling of software and hardware systems. Abstract State Machines, or ASMs, are well known for their versatility in computational and mathematical modeling of complex distributed systems with an orientation toward practical applications. They offer a good compromise between declarative, functional and operational views towards modeling of systems. The emphasis on freedom of abstraction in ASMs leads to intuitive yet accurate descriptions of the dynamic properties of systems. Since ASMs are in principle executable, the resulting models are validatable and possibly falsifiable by experiment. Finally, the well-defined notion of step-wise refinement in ASMs bridges the gap between abstract models and their final implementations. There is a variety of tools and executable languages available for ASMs, each coming with their own strengths and limitations. Building on these experiences, this work puts forward an extensible and executable ASM language and tool architecture, called CoreASM, emphasizing freedom of experimentation and design exploration in the early phases of the software development process. CoreASM aims at preserving the very idea of ASM modeling---the design of accurate abstract models at the level of abstraction determined by the application domain, while encouraging rapid prototyping of such abstract models for testing and design space exploration. In addition, the extensible language and tool architecture of CoreASM facilitates integration of domain-specific concepts and special-purpose tools into its language and modeling environment. CoreASM has been applied in a broad scope of R&D projects, spanning maritime surveillance, situation analysis, and computational criminology. In light of these applications, we argue that the design and implementation of CoreASM accomplishes its goals; it not only preserves the desirable characteristics of abstract mathematical models, such as conciseness, simplicity and intelligibility, but it also adheres to the methodological guidelines and best practices for ASM modeling.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".