MétaCan
Menu
Back to cohort
Record W2129479919 · doi:10.1109/ccece.2002.1013022

Case studies on translation of RTPA specifications into Java programs

2003· article· en· W2129479919 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceProgramming languageJavaCode generationFormal specificationProcess calculusSet (abstract data type)Software engineeringNotationProcess (computing)Operating system

Abstract

fetched live from OpenAlex

The real-time process algebra (RTPA) is a practical formal method that describes a software system, especially a real-time system, as a set of processes. RTPA can be used for system architectural specification, as well as for system static and dynamic behavior specifications. This paper presents a set of case studies on the generation of code based on RTPA specifications. The purpose of this work is to pilot the new approach and to test the feasibility of translation from RTPA specifications into Java programs. A set of fundamental abstract data types (ADTs) has been chosen in the case studies. The results demonstrate that code generation based on RTPA specifications is an encouraging and practical approach that supports precise code generation. As RTPA technologies provide an algebraic-based textual notation, it opens the way of automated translation from formal RTPA specifications into code in modem programming languages.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.168
GPT teacher head0.325
Teacher spread0.158 · 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

Quick stats

Citations7
Published2003
Admission routes2
Has abstractyes

Explore more

Same topicCognitive Computing and NetworksFrench-language works237,207