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Record W194824684 · doi:10.5555/2048476.2048484

DEVS for AUTOSAR platform modelling

2011· article· en· W194824684 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

VenueSpring Simulation Multiconference · 2011
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsMcGill University
Fundersnot available
KeywordsAUTOSARDEVSSoftware deploymentAutomotive industryComputer scienceEmbedded systemSoftwareArchitectureSoftware engineeringSoftware architectureSystems engineeringEngineeringOperating systemSimulationModeling and simulation

Abstract

fetched live from OpenAlex

AUTOSAR (AUTomotive Open System ARchitecture) is an open and standardized automotive software architecture, developed by automobile manufacturers, suppliers and tool developers. Its design is a direct consequence of the increasingly important role software plays in vehicles. As design choices during the software deployment phase may have a large impact on the real-time properties of the system, designers need a method to explore various trade-offs. In this paper we evaluate the appropriateness of DEVS, the Discrete-Event system Specification, for modelling and subsequent performance evaluation of AUTOSAR-based systems. We demonstrate and validate our work by means of a power window and ABS case study.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.531
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.155
GPT teacher head0.291
Teacher spread0.136 · 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