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Record W134794958

Model oriented programming: an empirical study of comprehension

2012· article· en· W134794958 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

VenueConference of the Centre for Advanced Studies on Collaborative Research · 2012
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceProgramming languageJavaComprehensionProgram comprehensionNotationNatural language processingSoftware engineeringSoftwareSoftware systemLinguistics
DOInot available

Abstract

fetched live from OpenAlex

Many tools and approaches support the use of modeling abstractions in textual form. However, there have been few studies about whether textual models are as comprehensible as graphical models. We present an experiment investigating the understandability of three different notations: Systems modeled in UML, and the same systems in both Java and Umple. Umple is a model-oriented programming technology that enhances languages like Java and PHP with textual modeling abstractions. It was designed to bridge the gap between textual and graphical modeling. Our experiment asked participants to answer questions reflecting their level of comprehension. The results reveal that for simple comprehension tasks, a visual model and a textual model are comparable. Java's comprehension levels were lowest of all three notations. Our results align with the intuition that raising the abstraction levels of common object-oriented programming languages enhances comprehensibility.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.231
GPT teacher head0.463
Teacher spread0.231 · 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