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Record W2151205770 · doi:10.1109/sysose.2007.4304242

Applying Model-Driven Development Techniques to the Development of Search and Rescue Systems

2007· article· en· W2151205770 on OpenAlexaff
Huy Pham, Alexander Ferworn, Qusay H. Mahmoud, Alireza Sadeghian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsToronto Metropolitan UniversityUniversity of Guelph
Fundersnot available
KeywordsComputer scienceDomain engineeringSoftware engineeringDomain (mathematical analysis)ExtensibilitySearch and rescueSoftware product lineSet (abstract data type)Model-driven architectureSoftware developmentSoftwareDomain analysisSoftware systemSystems engineeringComponent-based software engineeringProgramming languageSoftware constructionArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

This paper describes our work of applying modem software engineering methodologies such as model-driven development and product-line engineering to the development and maintenance of technical supporting systems in the domain of search and rescue. We propose an extensible, domain-specific, graphical modeling language and toolset that allow software developers of search and rescue systems to rapidly compose and generate their applications from a set of predefined graphical primitives that represent the common building blocks of SAR applications. A case study for the proposed toolset is also presented.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.757
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.001
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 head0.087
GPT teacher head0.335
Teacher spread0.248 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2007
Admission routes1
Has abstractyes

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