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Record W7000438456

Extending concern-oriented reuse to existing modelling languages

2020· dissertation· en· W7000438456 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

VenueeScholarship@McGill (McGill) · 2020
Typedissertation
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsReuseNotationAbstractionProcess (computing)Modeling languageSoftware developmentCode reuseFrame (networking)Domain-specific language
DOInot available

Abstract

fetched live from OpenAlex

Modern software systems constitute remarkably large and complex entities made up of an intricate web of components and libraries that render their development exclusively with source code ill-advised.Model-driven engineering promises to alleviate such issues by making models fundamental to all development phases.These models are to be specified according to appropriate and relevant modelling languages with the right level of abstraction while emphasizing a strict separation of concerns.MDE also emphasizes reusing existing standardized models and modelled design patterns to simplify the design process and increase productivity.Unfortunately, reuse is far from common in actual modelling practice, too often development teams will prefer using completely new models, at the cost of both time and effort, either because they have specific notation needs or are unaware of the potential benefits of reuse.Existing modelling frameworks and tools do little to facilitate this task, lacking intuitive and efficient reuse mechanisms and providing arcane interfaces to reuse and tune languages from the modelling community.Hence we propose, in the following thesis, our contribution to improve support for modelling reuse and language tailoring by extending the Concern Oriented Reuse (CORE) modelling framework to support multiple external languages and augmenting them with language independent reuse capabilities.Furthermore, with our proposed concept of perspectives, we would allow a language designer to tailor languages for a specific purpose.These perspectives also pave the road for concern-oriented multi-view modelling, as they can be designed to orchestrate the combined use of multiple languages to frame a design process.By redesigning and improving the existing reuse oriented CORE modelling framework through the addition of the language concept, we allow it to support any external abstract syntax, i.e. language, defined with a metamodel.Furthermore, CORE languages have a novel manner of describing their semantics through language actions, enabling our proposed concept of perspectives to i To Jrg, whose near-infinite wisdom, knowledge, wholesomeness and enduring patience form the main reason I was able to carry this project to fruition.To my dear parents, sister and brother, who stood by my, supported and helped me throughout all of my studies and projects.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.770
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0010.002
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 head0.038
GPT teacher head0.284
Teacher spread0.245 · 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