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Record W2345046200 · doi:10.5220/0005657301760183

Systematic Mapping Study of Model Transformations for Concrete Problems

2016· article· en· W2345046200 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTransformation (genetics)Computer scienceModel transformationDomain (mathematical analysis)Work (physics)Model-driven architectureScheme (mathematics)Data scienceRisk analysis (engineering)Operations researchUnified Modeling LanguageEngineeringArtificial intelligenceMathematicsSoftwareBusinessProgramming language

Abstract

fetched live from OpenAlex

As a contribution to the adoption of the Model-Driven Engineering (MDE) paradigm, the research community has proposed concrete model transformation solutions for the MDE infrastructure and for domain-specific problems. However, as the adoption increases and with the advent of the new initiatives for the creation of repositories, it is legitimate to question whether proposals for concrete transformation problems can be still considered as research contributions or if they respond to a practical/technical work. In this paper, we report on a systematic mapping study that aims at understanding the trends and characteristics of concrete model transformations published in the past decade. Our study shows that the number of papers with, as main contribution, a concrete transformation solution, is not as high as expected. This number increased to reach a peak in 2010 and is decreasing since then. Our results also include a characterization and an analysis of the published proposals following a rigorous classification scheme.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.226

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 head0.036
GPT teacher head0.248
Teacher spread0.213 · 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