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Record W2322384395 · doi:10.1145/2892664.2892697

On the modularization provided by concern-oriented reuse

2016· article· en· W2322384395 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
TopicAdvanced Software Engineering Methodologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsReuseComputer scienceSoftware engineeringMetamodelingModular programmingContext (archaeology)Separation of concernsStructuringSoftware developmentModel-driven architectureCore (optical fiber)AbstractionInterface (matter)Programming languageSystems engineeringSoftwareEngineering

Abstract

fetched live from OpenAlex

Reuse is essential in modern software engineering, and hence also in the context of model-driven engineering (MDE). Concern-Oriented Reuse (CORE) proposes a new way of structuring model-driven software development where models of the system are modularized by domains of abstraction within units of reuse called concerns. Within a concern, models are further decomposed and modularized by views and features. High-level concerns can reuse lower-level concerns, and models within a concern can extend other models belonging to the same concern, resulting in complex inter- and intra-concern dependencies. To clearly specify what dependencies are allowed between models belonging to the same or to different concerns, CORE advocates a three-part interface to describe each concern (variation, customization, and usage interfaces). This paper presents the CORE metamodel that formalizes the CORE concepts and enables the integration of different mod- elling languages within the CORE framework.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.448
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 science0.0010.000
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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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