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Record W1582789180 · doi:10.1002/spe.1155

Model‐driven rapid prototyping with Umple

2011· article· en· W1582789180 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

VenueSoftware Practice and Experience · 2011
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsUniversity of LethbridgeUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceFocus (optics)Interface (matter)Rapid prototypingProcess (computing)User interfaceClass (philosophy)Point (geometry)Human–computer interactionSoftware engineeringModeling languageSoftwareEngineeringProgramming languageOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

SUMMARY The emergence of model‐driven software development brings new opportunities and challenges for rapid prototyping. On the one hand, the modeling process is inherently abstract, removing the prototyper from details, and letting him or her focus on exploring design alternatives for various aspects of the system. On the other hand, the most popular modeling languages and tools entirely omit the modeling and generating of user interfaces. As a result, the benefit of user interface prototypes as a medium for interaction with the user and customer is lost. This paper presents a model‐oriented technology called Umple that can be used for prototyping and also supporting model driven engineering. Umple allows end users to quickly create class and state machine models and to incrementally embed implementation artifacts. At any point in the modeling process, users can quickly generate a fully functional prototype that exposes modeling implications on the user interface, and allows stakeholders to get a feel of how the full system will behave. Copyright © 2011 John Wiley & Sons, Ltd.

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: Methods
Teacher disagreement score0.638
Threshold uncertainty score0.761

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 communication0.0000.003
Open science0.0010.000
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 head0.041
GPT teacher head0.263
Teacher spread0.222 · 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