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Record W2098127690 · doi:10.1109/ccece.2005.1557336

An operational semantics for RTPA

2006· article· en· W2098127690 on OpenAlexaff
Cyprian F. Ngolah, Yingxu Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOperational semanticsComputer scienceProgramming languageSemantics (computer science)Process calculusFormal specificationSpecification languageLanguage Of Temporal Ordering SpecificationProcess (computing)Formal methodsAction semanticsFormal semantics (linguistics)Software engineeringDenotational semantics

Abstract

fetched live from OpenAlex

A formal specification methodology that describes the behaviors of a real-time system must have a means to verify the specification before software is implemented from it. Developing a verifier for a formal specification language demands an operational semantics for the language. If the syntactic constructs of the language involve variables, its operational semantics is more difficult to define since the environment must also show the association of variables and the values to which they are assigned. This paper presents the operational semantics of real-time process algebra (RTPA). RTPA describes a software system's behavior using a combination of meta-processes and process relations. Based on the meta-processes and process relations, the operational semantics of RTPA is presented, which shows how syntactic constructs can be reduced to values using inference rules. The operational semantics for RTPA will serve as a basis for the verifier and checker for RTPA specifications.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0050.010
Open science0.0020.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.023
GPT teacher head0.319
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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