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Record W2553786604 · doi:10.1002/spe.2463

Modelling a family of systems for crisis management with concern‐oriented reuse

2016· article· en· W2553786604 on OpenAlexaff
Omar Alam, Jörg Kienzle, Gunter Mussbacher

Bibliographic record

VenueSoftware Practice and Experience · 2016
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsMcGill UniversityTrent University
Fundersnot available
KeywordsReuseRotation formalisms in three dimensionsAbstractionComputer scienceSoftware engineeringDomain (mathematical analysis)Core (optical fiber)Risk analysis (engineering)Systems engineeringEngineeringBusiness

Abstract

fetched live from OpenAlex

Summary Concern‐oriented reuse (CORE) proposes the concern as a new unit of model‐based reuse encapsulating software artefacts pertaining to a domain of interest that span multiple development phases and levels of abstraction. With CORE, a concern encapsulates multiple reusable features, while allowing its generic models to be customized to problem‐specific contexts. We report on our experience of designing a family of crisis management systems (CMS) with the help of reusable concern libraries. The collected metrics show a considerable amount of reuse in our CMS design. The study provides encouraging evidence that CORE's vision to create large‐scale, generic and reusable entities that are expressed with the most appropriate modelling formalisms at the right level of abstraction is feasible. We present our experience in the design of the CMS and elaborate on the advantages as well as the efforts required to adopt CORE in an industrial setting. Copyright © 2016 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.058
GPT teacher head0.317
Teacher spread0.259 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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