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Record W1977372178 · doi:10.4018/jssmet.2010010102

Model-Driven Engineering of Service-Oriented Systems

2010· article· en· W1977372178 on OpenAlexaff
Dragan Gašević, Marek Hatala

Bibliographic record

VenueInternational Journal of Service Science Management Engineering and Technology · 2010
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsSimon Fraser UniversityAthabasca University
Fundersnot available
KeywordsComputer scienceSoftware engineeringRequirements engineeringContext (archaeology)Model-driven architectureSystems engineeringSoftware developmentService (business)Engineering managementService-oriented architectureBusiness processProcess managementEngineeringSoftwareWeb serviceWorld Wide WebWork in processBusinessOperations management

Abstract

fetched live from OpenAlex

Service-oriented architectures (SOA) are an essential platform to provide infrastructures that support widespread collaboration between organizations. These service-oriented systems are a new context for software developers, who must now be equipped with new development methods and technologies. This new context has specific requirements, such as better collaboration and communication between business users and software engineering across organizations and increased agility of the development and maintenance processes to better respond to newly emerged or changed requirements. In this paper, the authors present a research agenda that looks at the use of a novel software engineering discipline—model-driven engineering. By switching the focus from low-level technical details to high-level problem-specific details, model-driven engineering addresses challenges in the development of service-oriented systems. This paper particularly discusses the approach to the development of service-oriented systems based on business process modeling, which integrate business vocabularies and rules in different stages of the development lifecycle. Here, model-driven engineering can provide many promising solutions.

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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 teacher head, 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

Citations7
Published2010
Admission routes1
Has abstractyes

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